Why Quantum Computing Gets Special Attention In The Trump Administration’s Budget Proposal – Texas Standard

The Trump administrations fiscal year 2021 budget proposal includes significant increases in funding for artificial intelligence and quantum computing, while cutting overall research and development spending. If Congress agrees to it, artificial intelligence, or AI, funding would nearly double, and quantum computing would receive a 50% boost over last years budget, doubling in 2022, to $860 million. The administration says these two fields of research are important to U.S. national security, in part because China also invests heavily in these fields.

Quantum computing uses quantum mechanics to solve highly complex problems more quickly than they can be solved by standard or classical computers. Though fully functional quantum computers dont yet exist, scientists at academic institutions, as well as at IBM, Google and other companies, are working to build such systems.

Scott Aaronson is a professor of computer science and the founding director of the Quantum Information Center at the University of Texas at Austin. He says applications for quantum computing include simulation of chemistry and physics problems. These simulations enable scientists to design new materials, drugs, superconductors and solar cells, among other applications.

Aaronson says the governments role is to support basic scientific research the kind needed to build and perfect quantum computers.

We do not yet know how to build a fully scalable quantum computer. The quantum version of the transistor, if you like, has not been invented yet, Aaronson says.

On the software front, researchers have not yet developed applications that take full advantage of quantum computings capabilities.

Thats often misrepresented in the popular press, where its claimed that a quantum computer is just a black box that does everything, Aaronson says.

Competition between the U.S. and China in quantum computing revolves, in part, around the role such a system could play in breaking the encryption that makes things secure on the internet.

Truly useful quantum computing applications could be as much as a decade away, Aaronson says. Initially, these tools would be highly specialized.

The way I put it is that were now entering the very, very early, vacuum-tube era of quantum computers, he says.

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Why Quantum Computing Gets Special Attention In The Trump Administration's Budget Proposal - Texas Standard

Russian Quantum Center and Nissan have launched a project in the field of quantum chemistry – Quantaneo, the Quantum Computing Source

Modeling of complex systems such as materials, batteries, and medicines is extremely difficult for existing computers. The next generation of computing devices, which are quantum computers, will be able to solve such problems more efficiently. As a result, the business will be able to find practical solutions such as modeling of new materials, production of devices of a new class from such materials, and selection of optimal characteristics or reactions inside these materials, which are necessary for increasing the subsequent efficiency. One of the real challenges for the industry and business is the modeling of chemical compounds used in the batteries manufacturing process.

As part of the project, we are developing quantum chemistry methods using machine learning and quantum optimization. We plan to integrate the developed methods into the material design system, which is used today in Nissan. This will allow Nissan to unlock the huge potential of quantum computing for its tasks, and in the future, to achieve technological leadership, said Alexey Fedorov, Head of the Group Quantum Information Technologies RQC, Ph.D. in Theoretical Physics.

Quantum technologies are promising for solving many industrial challenges. The materials that can be created with quantum chemistry will significantly increase the power and capacity of batteries. As a result, we will get the opportunity to create highly efficient and environmentally-friendly transport, as well as new solutions. The future is behind these technologies and, together with our partner, Russian Quantum Center, we are striving to become a pioneer in this industry, said Shigeo Ibuka, Head of Nissan R&D center in Russia, Ph.D. in Physics.

In the long term, the use of quantum technologies will significantly reduce the time for the development of new materials, as well as predict their compliance with the requirements of industry and business. The RQC team will conduct research using both existing quantum computers and their own-developed quantum-inspired algorithms.

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Russian Quantum Center and Nissan have launched a project in the field of quantum chemistry - Quantaneo, the Quantum Computing Source

This Breakthrough Just Got Us One Step Closer to a Quantum Internet – Singularity Hub

While quantum computing tends to garner all the headlines, quantum technology also has huge promise for the communication networks of the future. Thats why on top of the roughly $450 million the Trump administration just earmarked for quantum research in their proposed budget, theres $25 million dedicated to building a nationwide quantum internet.

At what point a quantum network becomes the quantum internet is up for debate, but its likely to develop in phases of increasing sophistication, with the ultimate goal being a global network of quantum-connected quantum computers.

The US is well behind China on this front, though. A team led by quantum supremo Jian-Wei Pan have already demonstrated a host of breakthroughs in transmitting quantum signals to satellites, most recently developing a mobile quantum satellite station.

The reason both countries are rushing to develop the technology is that it could provide an ultra-secure communication channel in an era where cyberwarfare is becoming increasingly common.

Its essentially impossible to eavesdrop on a quantum conversation. The strange rules of quantum mechanics mean that measuring a quantum state immediately changes it, so any message encoded in quantum states will be corrupted if someone tries to intercept it.

But quantum states are also intrinsically fragile, which has made it difficult to establish quantum connections over large distances. But a team led by Pan has reported smashing the record for connecting two quantum memories in a paper in Nature.

Making a quantum connection relies on a phenomenon known as entanglement. If the states of two quantum objects are entangled, manipulating or measuring the state of one will be mirrored in the other. In theory this allows you to transmit quantum information instantaneously over very large distances.

So far most research has been done on entangled photonsincluding Pans work on quantum satellitesbut single particles can only carry limited information. Quantum memories, which are made up of clouds of millions of rubidium atoms, can store more, but the biggest distance theyd previously been entangled over was 1.3 kilometers.

Pans team came up with a clever workaround, as John Timmer explains in Ars Technica. Each quantum memory is set by shooting a photon at it, which causes the memory to emit another photon that is entangled with the state of the memory. This photon is then converted to an infrared wavelength so it can be transmitted over fiber optic cable.

The photons from each memory meet at a halfway point where they are measured in such a way that they become entangled. Because each was already entangled with their respective memories, these both become entangled as well, setting up the quantum connection.

The researchers carried out two experiments, one where they transmitted photons over 22 kilometers of cable buried underground between two separate facilities and one where they sent the particles around a 50-kilometer spool of optical cable in their lab.

The authors say those kinds of distances make it feasible to connect cities on a quantum internet and could be used to create quantum repeaters, a series of nodes that help boost the signal over longer distances.

But theres still some way to go. The process of converting the photons into a form that can travel along the fiber optic loses about 30 percent of the photons. The complex process involved in entangling the two photons also leads to further inefficiencies, which means theyre only able to successfully entangle photons roughly twice a second.

Thats a problem, because the memories only hold their state for 70 microseconds. The researchers admit they likely need to both boost the lifetime of the memories and the rate of entanglement for this approach to work in practice.

Its early, but the research is a significant step towards a quantum internet. If the US wants to play any part in its development, its going to have to play catch-up.

Image Credit: Garik Barseghyan from Pixabay

Excerpt from:
This Breakthrough Just Got Us One Step Closer to a Quantum Internet - Singularity Hub

Can All Of Bitcoin Be Hacked? – Forbes

$280 billion rides on the proposition that cryptocurrency is impregnable. Maybe it isnt.

Machinery in an IBM quantum computing lab (photo by Seth Wenig)

Call it the singularity. One day, maybe a decade from now, a message flashes across the internet: Elliptic curves cracked!

Elliptic curve cryptography, or ECC, is the foundation beneath bitcoin. Wouldnt the discovery of a hole in this code destroy the currencyand take down any coin exchange?

I posed the question to Brian Armstrong, who co-founded and runs Coinbase, the largest U.S. crypto exchange. He cant prove that there wont be some mathematical shortcut compromising bitcoin keys. But he considers the risk low.

Ten years in, there's a ton of people who have looked at this code, he answered, in an interview at the Coinbase headquarters in San Francisco. It's a hundred-billion-dollar bounty. So I think that scenario is very unlikely.

Bitcoin plus the lesser currencies that compete with it amount to a $280 billion asset pile, a tempting target for bad guys. From bitcoins earliest days, hacks, cracks, hijacks, phishes, vishes, and social engineering have threatened it. So far the successful assaults on this industry have been around the edges; even the big heist at Mt. Gox did not kill cryptocurrency.

But what if thieves discover a fundamental vulnerability? It might be in the way the encryption works. It might be in the global network of computer nodes that track ownership of bitcoin. It might be in some aspect of crypto that no one is thinking much about.

Crypto players offer two answers to the question about cosmic risks. One is that the system might see an asteroid coming and take defensive measures. If bitcoins 11-year-old encryption proves to have a weak spot, the nodes could move en masse to a different protocol. They might be able to do this before any coins have been stolen. Alternatively, they could hark back to an earlier version of the blockchain that was in place before a theft; this is how the Ethereum chain partly undid some skulduggery involving the DAO venture capital fund.

The other answer, not entirely reassuring, is that a lot more than bitcoin is at stake. Says Philip Martin, head of security for Coinbase: A core math problem? Were talking the collapse of the internet. Trillions of dollars course through electronic networks protected with encryption. So, for what its worth, in the digital apocalypse an implosion of bitcoin would be the least of our concerns.

Lets now consider some of the weaknesses that envelop digital currency.

Bad implementation

Once upon a time Sony used elliptic curves to protect its PlayStation. In order to run, a game would have to provide a digital signature constructed from Sonys secret key, the same kind of key that protects your bitcoin. The signature routine uses, as one of its inputs, a different randomly chosen number for each validating signature.

Sony goofed, recycling the same number. It turns out that this enabled anyone possessing two legitimate games and a knowledge of high-school algebra to compute the secret key and run pirated games. Andrea Corbellini, a cryptographer who has explained the flaw, speculates that Sony might have been inspired by this Dilbert cartoon.

You might think that all such potholes were found long ago and repaired. But no. Recently the National Security Agency reported on a flaw in a Microsoft browser that made a mistake in delivering the digital signatures that verify websites as legitimate. ECC calls for using a specific starting point. The flaw enabled a website to slip in a different point. With just the right substitute, a malicious site could have forged a signature and stolen the password for your bank account.

Microsoft quickly patched the hole. But it makes you wonder. Could there be other holes in some or all of the software used to hold and transfer virtual currencies?

Crypto managers are on guard. Says Martin, the Coinbase security guy: I am much more scared of an implementation flaw in a library than I am of a flaw in the underlying math.

Some bitcoin owners, trying to manage their own coin wallets, have made the same mistake Sony did with its game console. Writes one security expert: A lot of Russian bitcoin hackers have coded bots to automatically grab coins from vulnerable addresses. Presumably you have nothing to worry about if you hire experts to manage your wallet.

Social engineering

A crook doesnt have to know algebra to steal bitcoin. Good acting might do it.

Jamie Armistead is a vice president at Early Warning, the bank consortium that runs the Zelle payments network. Is there a risk that someone will crack the encryption that protects the money coursing through Zelle? Answers Armistead: Its not hacking that keeps him awake at night. Its phishing, like the false email to the corporate treasurer.

Vishing, a variant of phishing involving voice commands, is a security risk. So is device hijacking, in which the thief gets control of your smartphone account. So are all manner of man-in-the-middle attacks, the electronic version of a football pass interception. Cybersecurity engineers constantly update communication protocols to prevent that. They can barely keep up.

Could a hoax on a grand scale cause a majority of bitcoin nodes to simultaneously make a fatal mistake? It would have to be rather byzantine. Its conceivable.

Mathematical hacks

Encryption methods in common use look secure, because they have been studied for many years by many people. But they are not provably secure. Someone might discover a way to tunnel into them.

Encryption works by scrambling numbers. One way to do that, in the scheme named RSA (after inventors Rivest, Shamir and Adleman) that is still widely used to secure sensitive data, involves exponentiation and modular arithmetic. When you multiply 4 by itself 3 times, 3 is the exponent and you get 64. In modulo 11, you divide this by 11 and consider only the remainder 9.

With small numbers like these, this is a meaningless exercise. But cryptography uses gigantic numbers, and those numbers get shuffled into a giant mess. To get a sense of this, try out the exponentiation/modular game on our small numbers: 2 turns into 8, 3 into 5, 4 into 9 and so on. The only way to unshuffle is to know a certain secret about the modulo. This secret relates to some mathematical formulas that go back a long ways. A 17th century Frenchman named Fermat played an important role.

The other big shuffling scheme is ECC. This involves the modular multiplying of not single numbers but pairs of them. Think of the pair as the coordinates on a map. The multiplying is weird: To double a pair, you dont just move it twice as far from the corner; you bounce it off an elliptic curve. This scrambles all the points on the map. In cryptography, the starting point is not merely doubled; it is multiplied by a gigantic number. This really scrambles the map. That giant number, kept secret, is the key that unlocks a bitcoin.

RSA and ECC both have this feature: Someone who possesses the secret can prove that he possesses it without revealing it.

These two protection schemes rely on the apparent difficulty of certain arithmetic tasks. In the case of RSA, its finding the two numbers that were multiplied together to arrive at the modulo; in the case of ECC, its dividing the end point by the starting point to determine the multiplier. Difficult means taking trillions of years of guesswork on a laptop.

Unless shortcuts are found. For RSA, a well-known shortcut to factoring numbers involves a number sieve. For ECC, theres a big step, little step algorithm that dramatically reduces the computation time. At this point, these tricks go only so far. The difficulty, for a key of a given size, might be measured in billions rather than trillions of years.

For reassurance about the safety of the crypto market and of internet commerce we go back to what Brian Armstrong said: There is a large incentive to find a killer shortcut, and evidently no one has found one. But there is no way to know that no vastly better tricks are about to be discovered.

Fermat, the French mathematician, conjectured a simple fact about exponents of numbers that looked true but couldnt be proved. For three centuries people labored to prove it and failed. And then one day not too long ago a proof was discovered. It relied, in part, on elliptic curves.

Quantum computers

Computers using quantum effects could, in theory, shrink the time for decoding an encrypted message from billions of years to hours. One such theory, for cracking RSA, dates to 1994.

In October Google sent a shiver through the cryptography world by announcing quantum supremacy. An experimental quantum device, the company said, did in 200 seconds what would have taken a conventional computer 10,000 years. Thats debatable; some researchers at IBM claimed that Google overstated the time difference by six orders of magnitude. Still, quantum computing is a threat.

Not an immediate one. The task in the Google experiment was designed specifically for the limited skills of quantum computing elements. These skills are a long way from those needed to crack codes. The 1994 algorithm is not in use because the hardware for it exists only on paper.

But ten years from now? We dont know where quantum computing will be.

Back door

For an encryption routine the anonymous creator(s) of bitcoin plucked an elliptic curve off the shelf. This curve was designed by the federal government. Were the parameters devilishly selected in a way to create mathematical vulnerabilities? Does the National Security Agency have a back door to your coins? Probably not. But you cannot be sure. Governments are not in sympathy with the anarchist philosophy underlying cryptocurrency.

Since cryptos creation, thousands of coins have been pilfered in hacks, scams and Ponzi schemes. These will continue. As for the big knockover, in which the whole system is taken down, we can say that the probability is low. But it is not zero.

Related story: Guide To Cryptocurrency Tax Rules

Corbellinis primer

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Can All Of Bitcoin Be Hacked? - Forbes

QUANTUM COMPUTING TECHNOLOGIES Market: Comprehensive study explores Huge Growth in Future | D-Wave Systems Inc., IBM Corporation, Lockheed Martin…

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QUANTUM COMPUTING TECHNOLOGIES Market: Insights

Global Quantum Computing Technologies Market valued approximately USD 75.0 million in 2018 is anticipated to grow with a healthy growth rate of more than 24.0% over the forecast period 2019-2026. The Quantum Computing Technologies Market is continuously growing in the global scenario at significant pace. As it is recognized as a computer technology based on the principles of quantum theory, which explains the nature and behavior of energy and matter on the quantum level. A Quantum computer follows the laws of quantum physics through which it can gain enormous power, have the ability to be in multiple states and perform tasks using all possible permutations simultaneously. Surging implementation of machine learning by quantum computer, escalating application in cryptography and capability in simulating intricate systems are the substantial driving factors of the market during the forecast period. Moreover, rising adoption & utility in cyber security is the factors that likely to create numerous opportunity in the near future. However, lack of skilled professionals is one of the major factors that restraining the growth of the market during the forecast period.

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QUANTUM COMPUTING TECHNOLOGIES Market: Comprehensive study explores Huge Growth in Future | D-Wave Systems Inc., IBM Corporation, Lockheed Martin...

Quantum Internet Workshop Begins Mapping the Future of Quantum Communications – Quantaneo, the Quantum Computing Source

Building on the efforts of the Chicago Quantum Exchange at the University of Chicago, Argonne and Fermi National Laboratories, and LiQuIDNet (Long Island Quantum Distribution Network) at Brookhaven National Laboratory and Stony Brook University, the event was organized by Brookhaven. The technical program committee was co-chaired by Kerstin Kleese Van Dam, director of the Computational Science Initiative at Brookhaven, and Inder Monga, director of ESnet at Lawrence Berkeley National Lab.

The dollars we have put into quantum information science have increased by about fivefold over the last three years, Dabbar told the New York Times on February 10 after the Trump Administration announced a new budget proposal that includes significant funding for quantum information science, including the quantum Internet.

In parallel with the growing interest and investment in creating viable quantum computing technologies, researchers believe that a quantum Internet could have a profound impact on a number of application areas critical to science, national security, and industry. Application areas include upscaling of quantum computing by helping connect distributed quantum computers, quantum sensing through a network of quantum telescopes, quantum metrology, and secure communications.

Toward this end, the workshop explored the specific research and engineering advances needed to build a quantum Internet in the near term, along with what is needed to move from todays limited local network experiments to a viable, secure quantum Internet.

This meeting was a great first step in identifying what will be needed to create a quantum Internet, said Monga, noting that ESnet engineers have been helping Brookhaven and Stony Brook researchers build the fiber infrastructure to test some of the initial devices and techniques that are expected to play a key role in enabling long-distance quantum communications. The group was very engaged and is looking to define a blueprint. They identified a clear research roadmap with many grand challenges and are cautiously optimistic on the timeframe to accomplish that vision.

Berkeley Labs Thomas Schenkel was the Labs point of contact for the workshop, a co-organizer, and co-chair of the quantum networking control hardware breakout session. ESnets Michael Blodgett also attended the workshop.

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Quantum Internet Workshop Begins Mapping the Future of Quantum Communications - Quantaneo, the Quantum Computing Source

Please check your data: A self-driving car dataset failed to label hundreds of pedestrians, thousands of vehicles – The Register

Roundup It's a long weekend in the US, though sadly not in Blighty. So, for those of you starting your week, here's some bite-sized machine-learning news, beyond what we've recently covered, if that's your jam.

Check your training data: A popular dataset for training self-driving vehicles, including an open-source autonomous car system, failed to correctly label hundreds of pedestrians and thousands of vehicles.

Brad Dwyer, founder of Roboflow, a startup focused on building data science tools, discovered the errors when he started digging into the dataset compiled by Udacity, an online education platform.

I first noticed images that were missing annotations, Dwyer told The Register. That led me to dig in deeper and check some of the other images. I found so many errors I ended up going through all 15,000 images because I didnt want to re-share a dataset that had such obvious errors.

After flicking through each image, he found that 33 per cent of them contained mistakes. Thousands of vehicles, hundreds of pedestrians, and dozens of cyclists were not labelled. Some of the bounding boxes around objects were duplicated or needlessly oversized too.

Training an autonomous car on such an incomplete dataset could potentially be dangerous. The collection was pulled together to make it easier for engineers to collaborate and build a self-driving car. Thankfully, a project to develop such a system using this information seems to have died down since it launched more than three years ago.

Udacity created this dataset years ago as a tool purely for educational purposes, back when self-driving car datasets were very hard to come by, and those learning the skills needed to develop a career in this field lacked adequate training resources, a Udacity spokesperson told El Reg.

At the time it was helpful to the researchers and engineers who were transitioning into the autonomous vehicle community. In the intervening years, companies like Waymo, nuTonomy, and Voyage have published newer, better datasets intended for real-world scenarios. As a result, our project hasn't been active for three years.

We make no representations that the dataset is fully labeled or complete. Any attempts to show this educational data set as an actual dataset are both misleading and unhelpful. Udacity's self-driving car currently operates for educational purposes only on a closed test track. Our car has not operated on public streets for several years, so our car poses no risk to the public.

Roboflow has since corrected the errors on the dataset, and issued an improved version.

Standing up to patent trolls works: Mycroft AI, a startup building an open-source voice-controlled assistant for Linux-based devices, was sued for allegedly infringing a couple of patents, as we reported earlier this month.

Mycrofts CEO Joshua Montgomery spoke to The Register about his strong suspicions that he was being targeted by a so-called patent troll. His biz was told by a lawyer representing the patents' owner to cough up a license fee, and when Montgomery ignored the request, a patent-infringement lawsuit was filed against his company.

The mysterious patent owner, Voice Tech Corp, turned out to a brand new company in Texas, USA, and its address was someones bungalow, according to court filings. All of that fueled the growing speculation that, yes, Voice Tech Corp, was probably a patent troll.

Now, after facing sufficient resistance from Mycroft, Voice Tech Corp has dropped its case. Montgomery threatened to fight the lawsuit all the way to get Voice Tech Corps patents invalidated so that no other startup would have to face the same problem.

More Clearview drama: The controversial facial-recognition outfit that admitted to harvesting more than three billion publicly shared photos from social media sites is back in the news again.

The American Civil Liberties Union (ACLU) revealed it is trying to get Clearview to remove the claim from its marketing that its facial recognition code was verified using a methodology used by the ACLU. The rights warriors said they had no involvement in the product and do not endorse it. In fact, the union is pretty much against everything Clearview is doing.

Clearview boasts that its technology is 99 per cent accurate following numerous tests. Buzzfeed News, however, reckons it is nowhere near that good. The upstart previously said its algorithms helped police in New York City catch a terrorist planning to plant fake bombs on the subway. NYPD denied using Clearviews software.

Google, YouTube, Twitter, and Facebook have sent Clearview cease-and-desist letters demanding the startup stop scraping images of their platforms, and to delete those in its database. In a bizarre interview, Clearviews CEO fought back and said he believed that since all the photos were public, his stateside company, therefore, had a First Amendment right to public information." Er, yeah right.

Public funding for AI, 5G: President Donald Trump has vowed to spend more of US taxpayers' money on the research and development of emergent technologies, such as AI, quantum computing, and 5G, than traditional sciences.

The Budget prioritizes accelerating AI solutions, according to a proposal, subject to congressional approval, published this week. Along with quantum information sciences, advanced manufacturing, biotechnology, and 5G research and development (R&D), these technologies will be at the forefront of shaping future economies.

The Budget proposes large increases for key industries, including doubling AI and quantum information sciences R&D by 2022 as part of an all-of-Government approach to ensure the United States leads the world in these areas well into the future.

Trump pledged to spend $142.2bn in R&D for the next fiscal year, nine per cent less than this year. While AI and quantum computing are favored, there's less federal funding for general research and development for the other sciences.

The Department of Energy, the National Science Foundation, the National Institutes of Health, and others, will see cuts. The DOEs Advanced Research Projects Agency-Energy (ARPA-E) will be particularly hard hit: not only does the proposed budget effectively eliminate the agency, it must pay back $311m to the treasury.

You can read more about the proposed budget for the fiscal year of 2021, here.

CEO of AI startup steps down over allegations: The CEO of Clinc, a small artificial-intelligence outfit spun out of the University of Michigan, has resigned following claims he sexually harassed employees and customers.

Jason Mars, an assistant professor of computer science at the university, was accused of physically accosting clients, making lewd comments about female employees and interns, and hiring a prostitute during a work trip.

In an email to employees at Clinc, first reported by The Verge, Mars said the allegations against him were rife with embellishments and fabrications. He did, however, admit to drinking too much and partying with staff in a way thats not becoming of a CEO.

Sponsored: Detecting cyber attacks as a small to medium business

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Please check your data: A self-driving car dataset failed to label hundreds of pedestrians, thousands of vehicles - The Register

Daily AI Roundup: The Coolest Things on Earth Today – AiThority

Todays Daily AI Roundup covers the latest Artificial Intelligence announcements on AI capabilities, AI mobility products, Robotic Service, Technology from IBM, Comscore, Arista Networks, Cisco, Atos and DJI.

IBM and Delta Air Lines announced that the global airline is embarking on a multi-year collaborative effort with IBM including joining the IBM Q Network to explore the potential capabilities of quantum computing to transform experiences for customers and employees.

New research from Comscore, a trusted partner for planning, transacting and evaluating media across platforms, found that for the 4th month in a row, Toyota RAV4 was the most shopped new vehicle model market wide.

Arista Networks announced the acquisition of Big Switch Networks, a network monitoring and SDN (Software Defined Networking) company. Arista Networks provides a complete and visionary cloud networking suite, with rich capabilities in all critical areas of the campus, data center and public cloud.

Cisco announced that it has joined Facebooks Express Wi-Fi Technology Partner Program to close the digital divide and enable more people around the world to get connected to a faster, better internet.

Atos, a global leader in digital transformation, announced that it has expanded its collaboration with Microsoft to jointly address the fast-growing SAP HANA market, targeting the most demanding customers, many of whom are running mission-critical SAP workloads.

Talk of drones might have circled around military uses lately, but drones are actually being used to do good around the world. With its #DronesforGood campaign, DJIwishes to make people aware of the many ways in which drones can make our lives better and help keep us safe.

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Daily AI Roundup: The Coolest Things on Earth Today - AiThority

Quantum Computing Market 2020 Trends, Market Share, Industry Size, Opportunities, Analysis and Forecast by 2026 – Instant Tech News

Quantum Computing Market Overview:

Global Quantum Computing Market was valued at USD 89.35 million in 2016 and is projected to reach USD 948.82 million by 2025, growing at a CAGR of 30.02% from 2017 to 2025.

In the report, we thoroughly examine and analyze the Global market for Quantum Computing so that market participants can improve their business strategy and ensure long-term success. The reports authors used easy-to-understand language and complex statistical images, but provided detailed information and data on the global Quantum Computing market. This report provides players with useful information and suggests result-based ideas to give them a competitive advantage in the global Quantum Computing market. Show how other players compete in the global Quantum Computing market and explain the strategies you use to differentiate yourself from other participants.

The researchers provided quantitative and qualitative analyzes with evaluations of the absolute dollar opportunity in the report. The report also includes an analysis of Porters Five Forces and PESTLE for more detailed comparisons and other important studies. Each section of the report offers players something to improve their gross margins, sales and marketing strategies, and profit margins. As a tool for insightful market analysis, this report enables players to identify the changes they need to do business and improve their operations. You can also identify key electrical bags and compete with other players in the global Quantum Computing market.

Request a Report Brochure @ https://www.verifiedmarketresearch.com/download-sample/?rid=24845&utm_source=ITN&utm_medium=001

Top 10 Companies in the Quantum Computing Market Research Report:

QC Ware Corp., D-Wave Systems, Cambridge Quantum Computing, IBM Corporation, Magiq Technologies, Qxbranch, Research at Google Google, Rigetti Computing, Station Q Microsoft Corporation, 1qb Information Technologies

Quantum Computing Market Competition:

Each company evaluated in the report is examined for various factors such as the product and application portfolio, market share, growth potential, future plans and recent developments. Readers gain a comprehensive understanding and knowledge of the competitive environment. Most importantly, this report describes the strategies that key players in the global Quantum Computing market use to maintain their advantage. It shows how market competition will change in the coming years and how players are preparing to anticipate the competition.

Quantum Computing Market Segmentation:

The analysts who wrote the report ranked the global Quantum Computing market by product, application, and region. All sectors were examined in detail, focusing on CAGR, market size, growth potential, market share and other important factors. The segment studies included in the report will help players focus on the lucrative areas of the global Quantum Computing market. Regional analysis will help players strengthen their base in the major regional markets. This shows the opportunities for unexplored growth in local markets and how capital can be used in the forecast period.

Regions Covered by the global market for Smart Camera:

Middle East and Africa (GCC countries and Egypt)North America (USA, Mexico and Canada)South America (Brazil, etc.)Europe (Turkey, Germany, Russia, Great Britain, Italy, France etc.)Asia Pacific (Vietnam, China, Malaysia, Japan, Philippines, Korea, Thailand, India, Indonesia and Australia)

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Table of Content

1 Introduction of Quantum Computing Market

1.1 Overview of the Market1.2 Scope of Report1.3 Assumptions

2 Executive Summary

3 Research Methodology of Verified Market Research

3.1 Data Mining3.2 Validation3.3 Primary Interviews3.4 List of Data Sources

4 Quantum Computing Market Outlook

4.1 Overview4.2 Market Dynamics4.2.1 Drivers4.2.2 Restraints4.2.3 Opportunities4.3 Porters Five Force Model4.4 Value Chain Analysis

5 Quantum Computing Market, By Deployment Model

5.1 Overview

6 Quantum Computing Market, By Solution

6.1 Overview

7 Quantum Computing Market, By Vertical

7.1 Overview

8 Quantum Computing Market, By Geography

8.1 Overview8.2 North America8.2.1 U.S.8.2.2 Canada8.2.3 Mexico8.3 Europe8.3.1 Germany8.3.2 U.K.8.3.3 France8.3.4 Rest of Europe8.4 Asia Pacific8.4.1 China8.4.2 Japan8.4.3 India8.4.4 Rest of Asia Pacific8.5 Rest of the World8.5.1 Latin America8.5.2 Middle East

9 Quantum Computing Market Competitive Landscape

9.1 Overview9.2 Company Market Ranking9.3 Key Development Strategies

10 Company Profiles

10.1.1 Overview10.1.2 Financial Performance10.1.3 Product Outlook10.1.4 Key Developments

11 Appendix

11.1 Related Research

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About Us:

Verified market research partners with clients to provide insight into strategic and growth analytics; data that help achieve business goals and targets. Our core values include trust, integrity, and authenticity for our clients.

Analysts with high expertise in data gathering and governance utilize industry techniques to collate and examine data at all stages. Our analysts are trained to combine modern data collection techniques, superior research methodology, subject expertise and years of collective experience to produce informative and accurate research reports.

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TAGS: Quantum Computing Market Size, Quantum Computing Market Growth, Quantum Computing Market Forecast, Quantum Computing Market Analysis, Quantum Computing Market Trends, Quantum Computing Market

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Quantum Computing Market 2020 Trends, Market Share, Industry Size, Opportunities, Analysis and Forecast by 2026 - Instant Tech News

Nobody is behind in science and technology: Serguei Beloussov – Kuensel, Buhutan’s National Newspaper

The CEO of Acronis, a reputed global technology company, talks on the future of computers and opportunities

Ugyen Penjore

Artificial Intelligence, Quantum Computing, Internet of Things, Cloud Computing. Sounds too technically sophisticated and way beyond comprehension?

If that was what most in the audience felt when invited to a talk on Future of Computing, the guest speaker, Serguei Beloussov, left many convinced that the future is in science and technology.

Serguei Beloussov is the Chief Executive Officer of Acronis, a reputed global technology company and the founder of Schaffhausen Institute of Technology (SIT), Switzerland.

Bhutan should take advantage of its smallness and use it as an opportunity to get ahead in the field of science and technology, said Serguei Beloussov, who is also a serial tech entrepreneur. Drawing examples of Switzerland where his company SIT is located and Singapore where he is currently based, he said Switzerland was a small poor farming country before it started precision manufacturing. Singapore, he said, transformed from a small poor country to one of the wealthiest nation based on science and technology.

The smallness is not a threat. It is an opportunity. Science could be the future for Bhutan, he said.

In technology and science, the number of people, he said, was not important. It is about having smart people. Albert Einstein in one year did more to science than all the 30million PhD holders did in 50 years, he said. Bhutan, he added, has the advantage given its culture and ethics. In science and technology, ethics matters.

On the importance of quantum computers, Serguei Beloussov, who is also the first man to bring cyber protection to motorsports, said the world was becoming digital whether we want it or not. The world is transforming from primarily a physical world of the past to the digital world of the future. The world is now about Internet of things, next generations computers, big data, virtual reality and space exploration.

On how and where Bhutan could start, Serguei Beloussov, said IT is an amazing field where everything changes every 10 years. There is no way that you are behind because there are many aspects to IT that are new. You can just start new and will be starting fresh with everyone else, he said.

Serguei Beloussov said real-world problems need computers to solve it. The problems of aging and diseases, environment and global warming, social justice and poverty could be solved with better computers, he said. If we have the right computers, we could predict the problems of the universe. Quantum computers are a reality and there are amazing features to solve once unsolvable problems.

The digital world, however, is fragile and needs security. Therefore, cyber protection has become the basic need in the digital world. Without cyber protection, we cannot continue in the future just like we cannot continue living without immune system. Immune system for the digital world is cyber protection.

The CEO said cybersecurity was a priority for Bhutan too. There is no choice. Whether you wan to be happy or unhappy, you have to have cyber security as you have gone digital, he said. For Bhutan, cyber security is more important given our geographic location.

On the apprehension that supercomputers or building the next generation of computers would require resources, human and material, Serguei Beloussov said science and technology are not really difficult or complicated as many believe although a lot of symbols and technical data are involved. Sixty years ago when people were using information theory and computer science, it was for scientists. Today, it is for everyone. Quantum physics actually simpler than classical physics. In fact, it is not harder to learn it than arithmetic, he said. 300 years ago, only the priests could read and they are considered special people. Today everybody could read, write and count. This is no harder.

GNH and the digital world

Calling himself a believer in knowledge, Serguei Beloussov said that knowledge could make people happier when asked about how the drive for technology featured in the concept of Gross national Happiness. I believe that without knowledge, you will be unhappy. And so, if you are refusing technology, its effectively refusing knowledge.

Bhutanese, he said, were a lot happier than others, but the ranking was not high on the happiness ranking. He pointed out issues related to unemployment. People want employment. In my country, we have 100 percent employment and people are happy. I dont think you can argue that you want to have less jobs, he said.

On GDP, Serguei Beloussov said the world cares about GDP. People want to be having higher levels of life. Everybody wants to have a nice house, live longer lives, they to be sick less, get better education, he said.

In my opinion, if you increase the life of a person, you provide them better education, better schools, better healthcare, better roads, better food, better environment, cleaner forest.

IT hub in Bhutan?

At the talk, the CEO said that SIT was considering establishing a South Asia campus in Bhutan. Although it is at an initial stage, the founder of SIT said that leveraging on IT could create high-end jobs, attract high-end tourists and promote local industries and the government.

He said that just 10,000 IT jobs in the country could add about 30,000 non-IT jobs, develop Bhutanese tech companies and add about Nu 3Billion to the GDP.

He also said that SIT campuses could create leaders. The first approach for a SIT campuses in the country would be approaching Cyber security, Atificial Intelligence and machine learning, software engineering and robotics in the field of computers.

In the field of business, the approach is on digitilising health, new generation business management, digital sports digital learning and education and artificial intelligence in arts and design.

The talk on Tuesday, February 11, was organised by His Majestys secretariat at Taj Hotel, Thimphu.

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Nobody is behind in science and technology: Serguei Beloussov - Kuensel, Buhutan's National Newspaper

The quantum computer is about to change the world. Three Israelis are leading the revolution – Haaretz

In October 2019, Google announced that its quantum computer, Sycamore, had done a calculation in three minutes and 20 seconds that would have taken the worlds fastest supercomputer 10,000 years. Quantum supremacy, Google claimed for itself. We now have a quantum computer, it was saying, capable of performing calculations that no regular, classical computer is capable of doing in a reasonable time.

Where do you buy a computer like that? You dont. Googles Sycamore cant run Word or Chrome, it cant even run a nice friendly game of Minesweeper. In fact, Googles supreme quantum computer doesnt know how to do anything, other than perform one useless calculation. It resembles the huge computer in The Hitchhikers Guide to the Galaxy, which came up with the calculation of 42, as the Answer to the Ultimate Question of Life, the Universe, and Everything although no one knows what the question is.

The question is now being worked on in Tel Aviv, on Derech Hashalom Street. In their generic office in the citys Nahalat Yitzhak neighborhood, three physicists who received their doctorates at Rehovots Weizmann Institute of Science Nissim Ofek, 46; Yonatan Cohen, 36; and Itamar Sivan, 32 are developing instruments of control that will tame the quantum monster.

Ten years ago, when I took a course in quantum computing, it was considered science fiction, Dr. Sivan, the CEO of their company, Quantum Machines, relates. The experts said that it wouldnt happen in our lifetime or may never happen. As a physicist, quantum computing is a dream come true. Almost all our employees are physicists, even those who work as programmers, and most of them approached us. They read about an Israeli company for quantum computing and simply couldnt restrain themselves. Theres nothing more exciting than to learn for years about Schrdingers cat and about all the wild quantum effects, and then to enter a laboratory and actually build Schrdingers cat and leverage the theory into a prodigious force of calculation.

Already in high school, Sivan, who was born and raised in Tel Aviv, knew that he was drawn to the mysterious world of elusive particles. I did honors physics, and in that framework we learned a little quantum mechanics. Without mathematics at that stage, only the ideas of quantum mechanics. My brain took off. The quantinizing of the world, of the space around me, was very tangible. I felt that I understood the quantum world. Afterward I understood that I didnt understand anything, but thats not important. Its preferable to develop an intuition for quantum at an early age like for a language. Afterward I did military service, but I didnt forget that magic.

I was a bureau chief [i.e., military secretary], not the most intellectually challenging job in the army, he continues, and I was afraid that when I was discharged, I would be too old. You know, its said that all the great mathematicians achieved their breakthroughs before the age of 25. So, in parallel with army service I started undergraduate studies at the Open University. On the day after my discharge, I flew to Paris to continue my studies at the cole Normale Suprieure because there are a few other things that are also worth doing when youre young, such as living in Paris.

He met his partners in the project, Nissim Ofek and Yonatan Cohen, at the Weizmann Institute, where they all studied at the Center for Submicron Research, under Prof. Moty Heiblum.

Sivan: Nissim had completed his Ph.D. and was doing a postdoc at Yale just when Yonatan and I started. At the same time, Yonatan and I established the Weizmann Institutes entrepreneurship program. When we graduated, we asked each other: Okay, what do we know how to do in this world? The answer: quantum electronics and entrepreneurship. We really had no choice other than to found Quantum Machines.

QM is a singular startup, says Prof. Amir Yacoby, a Harvard University physicist and a member of the companys scientific advisory board. A great many startups promise to build ever more powerful quantum computers. QM is out to support all those ambitious platforms. Its the first company in the world that is building both the hardware and the software that will make it possible to use those computers. You have to understand that quantum computing was born in university labs before the electronics industry created designated devices for it. What we did was to take devices designated for classical computers and adapt them to the quantum computers. It took plenty of student years. Thats why QM looks so promising. These guys were the wretches who went through hell, who learned the needs the hard way. Today, every research group that Im familiar with is in contact with them or has already bought the system from them. QM is generating global enthusiasm.

Well return to the Israeli startup, but first we need to understand what all the fuss is about.

What we refer to as the universal computing machine was conceived by the man considered the father of computer sciences, Alan Turing, in 1936. Years before there were actual computers in the world, Turing suggested building a read-write head that would move a tape, read the different state in each frame, and replicate it according to commands it received. It sounds simplisltic, but there is no fundamental difference between the theoretical Turing machine and my new Lenovo laptop. The only difference is that my Turing machine reads-writes so many frames per second that its impossible to discern that its actually calculating. As the science-fiction writer Arthur C. Clarke put it, Any sufficiently advanced technology is indistinguishable from magic.

Classical computers perform these calculations by means of transistors. In 1947, William Shockley, Walter Brattain and John Bardeen built the first transistor the word is an amalgam of transfer and resistor. The transistor is a kind of switch that sits within a slice of silicon and acts as the multi-state frame that Turing dreamed of. Turn on the switch and the electricity flows through the transistor; turn it off, and the electricity does not flow. Hence, the use of transistors in computers is binary: if the electricity flows through the transistor, the bit, or binary digit, is 1; and if the current does not flow, the bit is 0.

With transistors, the name of the game is miniaturization. The smaller the transistor, the more of them it is possible to compress into the silicon slice, and the more complex are the calculations one can perform. It took a whole decade to get from the one transistor to an integrated circuit of four transistors. Ten years later, in 1965, it had become possible to compress 64 transistors onto a chip. At this stage, Gordon Moore, who would go on to found Intel, predicted that the number of transistors per silicon slice would continue to grow exponentially. Moores Law states that every 18 months, like clockwork, engineers will succeed in miniaturizing and compressing double the number of transistors in an integrated circuit.

Moores Law is a self-fulfilling fusion of a natural law and an economic prediction. A natural law, because miniaturized electrical circuits are more efficient and cheaper (its impossible to miniaturize a passenger plane, for example); and an economic law, because the engineers bosses read Moores article and demanded that they compress double the number of transistors in the following year. Thus we got the golden age of computers: the Intel 286, with 134,000 transistors in 1982; the 386, with 275,000 transistors, in 1985; the 486, with 1,180,235 transistors, in 1989; and the Pentium, with 3.1 million transistors, in 1993. There was no reason to leave the house.

Today, the human race is manufacturing dozens of billions of transistors per second. Your smartphone has about 8.5 billion transistors. According to a calculation made by the semiconductor analyst Jim Handy, since the first transistor was created in 1947, 2,913,276,327,576,980,000,000 transistors thats 2.9 sextillion have been manufactured, and within a few years there will be more transistors in the world than all the cells in all the human bodies on earth.

However, the golden age of the transistors is behind us. Moores Law ceased being relevant long ago, says Amir Yacoby. Computers are continuing to be improved, but the pace has slowed. After all, if wed continued to miniaturize transistors at the rate of Moores Law, we would have reached the stage of a transistor the size of an atom and we would have had to split the atom.

The conventional wisdom is that the slowdown in the rate of the improvement of classic computers is the engine driving the accelerated development of quantum computers. QM takes a different approach. Theres no need to look for reasons to want more computing power, Sivan says. Its a bottomless pit. Generate more calculating power, and we will find something to do with it. Programmers are developing cooler applications and smarter algorithms, but everything rests on the one engine of calculating power. Without that engine, the high-tech industry would not have come into being.

Moores Law, Cohen adds, starts to snafu precisely because miniaturization brought us to the level of solitary atoms, and the quantum effectsare in any case already starting to interfere with the regular behavior of the transistors. Now we are at a crossroads. Either we continue to do battle against these effects, which is what Intel is doing, or we start harnessing them to our advantage.

And theres another problem with our universal Turing machine: even if we were able to go on miniaturizing transistors forever, there is a series of hard problems that will always be one step ahead of our computers.

Mathematicians divide problems according to complexity classes, Cohen explains. Class P problems are simple for a classic computer. The time it takes to solve the problem increases by polynomials, hence the P. Five times three is an example of a polynomial problem. I can go on multiplying and my calculating time will remain linear for the number of digits that I add to the problem. There are also NP problems, referring to nondeterministic polynomial time. I give you the 15 and you need to find the primary factors five times three. Here the calculating time increases exponentially when the problem is increased in linear terms. NP complexity problems are difficult for classic computers. In principle, the problem can still be solved, but the calculating time becomes unreal.

A classic example of an NP complexity problem is that of the traveling salesman. Given a list of cities and the distance between each two cities, what is the shortest route for the traveling salesman who in the end has to return to his hometown to take? Between 14 cities, the number of possible routes is 10 to the 11th power. A standard computer performs an operation every nanosecond, or 10 to the 9th power operations per second, and thus will calculate all the possible routes in 100 seconds. But if we increase the number of cities to just 22, the number of possibilities will grow to 10 to the 19th power, and our computer will need 1,600 years to calculate the fastest route. And if we want to figure out the route for 28 cities, the universe will die before we get the result. And in contrast to the problem that Googles quantum supremacy computer addressed, the problem of the traveling salesman comes from the real world. Airlines, for example, would kill to have a computer that could do such calculations.

In fact, modern encrypting is based on the same computer-challenging problems. When we enter the website of a bank, for example, the communication between us and the bank is encrypted. What is the sophisticated Enigma-like machine that prevents outsiders from hacking into our bank account? Prime numbers. Yes, most of the sensitive communication on the internet is encrypted by a protocol called RSA (standing for the surnames of Ron Rivest, the Israeli Adi Shamir, and Leonard Adelman), whose key is totally public: breaking down a large number into prime numbers. Every computer is capable of hacking RSA, but it would take many years for it to do so. To break down a number of 300 digits into prime numbers would require about 100 years of calculation. A quantum computer would solve the problem within an hour and hack the internet.

The central goal of the study of quantum algorithms in the past 25 years was to try and understand what quantum computers could be used for, says Prof. Scott Aaronson, a computer scientist from the University of Texas at Austin and a member of QMs scientific advisory board. People need to understand that the answer is not self-evident. Nature granted us a totally bizarre hammer, and we have to thank our good fortune that we somehow managed to find a few nails for it.

Spooky action

What is this strange hammer? Without going deeply into quantum theory, suffice it to explain that quantum mechanics is a scientific theory that is no less grounded than the Theory of General Relativity or the theory of electricity even if it conflicts sharply with common sense. As it happens, the universe was not tailor-made for us.

Overall, quantum mechanics describes the motion of particles in space. At about the same time as Turing was envisioning his hypothetical computer, it was discovered that small particles, atomic and sub-atomic, behave as if they were large waves. We will illuminate two cracks with a flashlight and we will look at the wall on the other side. What will we see? Bands of light and shade alternately. The two waves that will be formed in the cracks will weaken or strengthen each other on the other side like ocean waves. But what happens if we fire one particle of light, a solitary photon, at the two cracks? The result will be identical to the flashlight: destructive and constructive interference of waves. The photon will split in two, pass through the two cracks simultaneously and become entangled with itself on the other side.

Its from this experiment, which was repeated in numberless variations, that the two odd traits of quantum mechanics are derived: what scientists call superposition (the situation of the particle we fired that split into two and passed between the two cracks in parallel) and the ability to predict only the probability of the photons position (we dont know for certain where the particle we fired will hit). An equally strange trait is quantum entanglement. When two particles are entangled, the moment one particle decides where it is located, it influences the behavior of the other particles, even if it is already on the other side of the cracks or on the other side of the Milky Way. Einstein termed this phenomenon spooky action at a distance.

The world of quantum mechanics is so bizarre that its insanely attractive, Sivan suggests. On the one hand, the results contradict common sense; on the other hand, it is one of the most solidly grounded theories.

The best analogy was provided by the physicist Richard Feynman, who conceived the idea of a quantum computer in 1982, notes Cohen. Feynman compared the world to a great chess game being played by the gods We do not know what the rules of the game are; all we are allowed to do is to watch the playing. Of course, if we watch long enough, we may eventually catch on to a few of the rules.

According to Cohen, Until the beginning of the 20th century, physicists could only look at pawns at the binary moves. Quantum mechanics shows us that there is a larger and far more interesting set of laws in nature: there are knights, rooks, queens.

Here, adds Sivan, pointing, this table here has an end, right? No, it doesnt. Like the particle that passes through the cracks, this table also has no defined size in space, only probability. The prospect is that we will find a table particle fading exponentially at the edge of the table. In order to work with the table on an everyday basis, we can make do with the classic, simplistic description. But our world is a quantum world and we need to know how to describe it truly. And for that we need quantum computers. In order to describe a simple molecule with 300 atoms penicillin, lets say we will need 2 to the 300th power classic transistors which is more than the number of atoms in the universe. And that is only to describe the molecule at a particular moment. To run it in a simulation would require us to build another few universes, to supply all the material needed.

But humanity is today running simulations on whole galaxies.

Sivan: True, but humanity is really bad at that. We are simplifying, cutting corners. This table will have a boundary in a simulation, so that you can work with it. The galaxy you are simulating is composed of molecules that behave according to quantum mechanics, but in the simulation you will run, the galaxy having no other choice will operate according to the principles of classical mechanics. That was Feynmans great insight: We cannot simulate a quantum world with classical computers. Only a quantum computer will know how to simulate a quantum system.

Feynman didnt stop at imagining a machine that would depict or simulate a quantum system that is, a computer that would be analogic for a quantum system. He took a step forward and asked: Why not build a universal quantum calculating machine? The theoretical principles for the universal quantum computer were set forth by the Israeli-born physicist David Deutsch in 1985. A quantum computer, Deutsch stated, will not be comparable to a Turing machine; it will be capable of solving every problem that a Turing machine is capable of solving and another few problems, too. Such as NP complexity problems.

Classic computers are based on binary bits, two states, 0 or 1, Cohen says. But like the particle in the experiment, Schrdingers cat can also be in a superposition, both dead and living, both 0 and 1. We dont know how to do that with cats yet, but there are systems that we can bring to superposition. Every such system is called a quantum bit, or qubit. Of course, the superposition will ultimately collapse, because we need to see the result on the other side, but along the way the cat was both living and dead, the lone photon truly passed through both cracks with the result in accordance.

Sivan: Two classic bits can take four possible combinations: 00, 01, 10 or 11. Two quantum bits can be in all four of those combinations simultaneously: 00, also 01, also 10 and also 11. With eight qubits you reach 256 combinations. That is true exponential force. Lets say you have a processor with a billion transistors, a billion bits, and you want to double its memory. You would have to add another billion bits. To double the memory in a quantum computer you will have to add one qubit.

How does it work? Take, for example, two simple calculations with two classic bits. In the first calculation you feed 00 into the machine and the algorithm says to the computer to switch, or turn over, the first bit, so we get 01. Then we want to solve another problem. We feed into the computer two bits in a 11 state, and the computer turns over the second bit, so we get 10. Two calculations, two operations. Now we will entangle a pair of quantum bits in superposition: they are both 00 and 11. Instead of two operations, the quantum computer will turn over the second bit and we will get both 01 and 10. Two calculations, one operation. And the operation will continue to be one, no matter how many calculations we perform. If in the classic computer, we are at any given moment in one state out of two states, 0 or 1, to the power of the number of bits we have, in the quantum computer we are at any given moment in each of the states.

An important clarification is in order here. Scott Aaronsons blog, called Shtetl-Optimized, carries the motto, Quantum computers would not solve hard search problems instantaneously by simply trying all the possible solutions at once. Thats because a quantum computer can be in all the states at every given moment but we, by heavens grace, are not quantum beings. We need an answer. That is why scientists are building the quantum computer with delicate choreography so that all the mistaken calculations will weaken one another and the calculations that contribute to the right answer will empower one another so that we non-quantum mortals will, with high probability, be able to measure the right answer from among the random nonsense.

Almost every popular article is wrong on this point, Prof. Aaronson explains. Like Sisyphus rolling the boulder up the hill, I have been trying for 15 years to explain that if we simply measure the superposition of each of the possible answers, we will get a random answer. For that we dont need quantum computers you can flip a coin or spin a top. All the hopes we are pinning on quantum computing depend on our ability to increase the probability of the right answer and reduce the probability of all the wrong answers.

Thus, the classic bit is encoded through an electrical current in semiconductors, so that if the current does not flow we get 0, and if it does flow we get 1. The revolution of the quantum computer hasnt yet determined what the best way is to encode quantum bits, but at the moment the most advanced quantum computers are using a two-atom electron. The electron can be either in atom left, 0, or in atom right, 1 or in both of them, in superposition at the same time. Googles Sycamore has 53 such qubits, fewer than the number of classical bits there were in the world when Moore formulated his law in 1964. All the giants such as IBM, Intel, Microsoft and Alibaba are in the quantum race to add qubits; the experts think that in a year or two we will see quantum computers with 100 or 200 qubits. The rate of increase is astounding, appropriate for a quantum Moores Law. Now arises the question: If one qubit works, and 53 qubits work together, why not create more qubits? Why not create a processor possessing hundreds, thousands, millions of qubits, to hack the RSA encryption of all the banks in the world and retire on a yacht?

The answer is that quantum computers make mistakes. Classical computers make mistakes, too, but were not aware of that because the classical computers also correct the mistakes. If, for example, a calculation is run on three classical bits, and one bit produces the result 0, and two bits produces the result 1, the processor will determine that the first bit was wrong and return it to state 1. Democracy. In quantum computing, democracy doesnt work, because the voters entered the polling booth together. Think of three cubits entangled to 000 and to 111, which is to say, three electrons that are present together both in the left atom and in the right atom simultaneously. If the third bit turns over by mistake, we will get a state of 001 and 110. If we try to correct the mistake, or even to check whether a mistake occurred, our superposition will collapse immediately and we will get 000 or 111. In other words, the qubits defeat themselves. The quantum entanglement that makes the computer marvel possible is the same one that precludes the possibility of adding more qubits: The electrons simply coordinate positions, so that it is impossible to ask them who made the mistake. That is a problem, because qubits are notorious for their sensitivity to the environment and there are also prone to make mistakes a lot more than regular bits.

Classical bits do not have a continuum of possibilities, Prof. Yacoby notes. What is a classical bit? The electricity flows or doesnt flow. Even if the current weakens or becomes stronger, it is still considered a current. The quantum bits are sequential, the electron can be largely in atom right and partially in atom left. That is their strength and that is their weakness. Therefore, every interaction with the environment affects them dramatically. If I use my regular computer and an electronic wave passes through the transistor, the state of the bit does not change. The same electronic wave passing through a qubit will cause loss of the qubits coherence, memory. The information will leak out to the surroundings and we will not be able to reconstruct it.

For this reason, we will not see quantum iPads in the near or distant future. A classical processor performs a calculation in a nanosecond, but will preserve the information for days, months, years ahead. A quantum computer also performs a calculation in a nanosecond and at best will manage to preserve the information for a hundredth of a microsecond. Quantum computers are so sensitive to external interference that they must be isolated from their surroundings at almost minus 273 degrees Celsius, one 10,000th of a degree above absolute zero.

The interaction of the qubits with the environment is a serious problem, because they lose the memory, says Yacoby. But that only means that they are measuring something in regard to the environment. There is a whole field of quantum sensors that enable us to learn about traits of materials with psychopathic sensitivity. Quantum clocks can measure a change in the force of gravity of the Earth from my nose to my chin. Its unbelievable. Lockheed Martin is developing a cruise missile that will be able to navigate itself without GPS, solely according to the quantum sensitivity to minute differences in Earths magnetic field. And there are quite a few startups that use quantum sensors to identify cancerous cells. These are applications for which I foresee commercial success long before we actually have quantum computers.

Theres also another game that can be played with quantum sensitivity: encryption. A quantum computer can hack the widespread encryption protocol on the internet, RSA, because it can calculate NP problems with no problem. But given that superposition collapses the moment the black box is opened to examine whether the cat is dead or alive, a quantum encryption protocol will be immune by virtue of its being quantum. Communication with the bank can be left open on a quantum server. Anyone who tries to listen to the line will cause the collapse of the superposition and hear gibberish and the bank and the client will know that someone listened in.

But with all due respect to the benefit that can be extracted from the fact that quantum computers dont work but can only sense humanity will benefit tremendously if we can make them work. In our world, everything is quantum at its base. Mapping the structure of chemical molecules requires quantum computing power, and we will know how to ward off diseases only when the pharmaceutical companies are able to run quantum simulations. The neurons in our brain are quantum, and we will be able to create true artificial intelligence only when we have quantum computers that can run independent thoughts.

Its not the race to the moon, Cohen says, its the race to Mars. In my opinion, the greatest scientific and engineering challenge now facing the human race is the actualization of quantum computers. But in order to actualize all those dreams, we need to understand how we correct errors in qubits, how we control them. Thats what were doing. QM is the first company in the world that is totally focused on developing control and operating systems for quantum computers. The system we are developing has a decisive role in correcting errors. In fact, the third founder of QM, Nissim, was the first person in the world to prove that errors in quantum bits can be corrected. He didnt show it on paper he proved it, succeeded, demonstrated it. Instead of measuring every qubit and seeing which was wrong, its possible to examine whether the qubits are in the same state. If one qubit is in a different state, well know that it is wrong. You can know whether you voted for a party that didnt win without knowing the results of the election.

QM was founded in 2018 with the aim of bypassing the problem of errant qubits with the help of some old friends: classical bits. If the classical computer contains hardware and software, meaning a great many transistors and a language that tells the processor which calculations to run on them, in a quantum computer, the cake has three layers: quantum hardware (that is, qubits), classical hardware that will be able to operate the quantum hardware, and software (both classical and quantum). That is our way of having an impact on the qubits while reading the results in our world, Sivan says. If we were quantum beings, we would be able to speak directly with the computer but were not.

Would you like to be a quantum being? It would save you a lot of work.

Yes, but then the other quantum beings wouldnt buy our products.

QM is building the classical hardware and software that will be able to send the right electric signals to the electrons and to read the results with minimal interference to the black wonder box. Their integrated system is called the Quantum Orchestration Platform.

Today there is separate hardware for every individual quantum computer, Cohen says. We are building an orchestra system that can work with every such computer and will send the most correct electrical signals to the qubits. In addition, we are developing programming language that will make it possible for us to program the algorithms the commands. Thats a general quantum language, like C [programming language]. Today there is a potpourri of languages, each quantum computer and its language. We want our language, QUA, to be established as the standard, universal language for quantum computing.

Sound off the wall? Not all that much. Last month, QM joined the IBM Q Network, in an attempt to integrate the computer conglomerates programming languages into the Quantum Orchestration Platform of Sivan and his colleagues, and to publish a complete complier (a complier is a computer program that can translates computer code written in one programming language into another language) by the second quarter of 2020. The complier will be able to translate every quantum programming language into the QM platform. Thus, an algorithm written in a university in Shanghai will be able to run on a quantum computer built in Googles laboratories in, say, Mountain View.

Says Yonatan Cohen: The major players, like Google and IBM, are still gambling. They are developing a quantum processor that is based on their own [singular] technology. And it could be that in a few years we will discover a better platform, and their processor will not have any use. We are building a system that is agnostic to quantum hardware. Our goal is to grow with the industry, no matter what direction it develops in. Because the underlying assumption is that you dont know exactly when quantum computers will start to be practicable. Some people say three years, others say 20 years. But its clear to us that whoever is in the forefront when it erupts will win bigtime, because he will control the new computing force. Everyone will have to work with him, in his language, with his hardware.

Sivan: Its possible that in another few years, we will look back on this decade and see an unexampled technological turning point: the moment when quantum computers went into action. Thats not another technological improvement. Its a leap

A quantum leap!

Sivan: Exactly.

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The quantum computer is about to change the world. Three Israelis are leading the revolution - Haaretz

DOE Workshop Begins Mapping the Future of Quantum Communications – insideHPC

Paul Dabbar Quantum Internet WorkshopPaul Dabbar, Under Secretary of Energy for the DOEs Office of Science, gives the welcoming remarks at the Quantum Internet Blueprint Workshop, held Feb. 5-6 in New York City.

The U.S. Department of Energys Office of Science, under the leadership of Under Secretary of Energy Paul Dabbar, sponsored around 70 representatives from multiple government agencies and universities at the firstQuantum Internet Blueprint Workshop, held in New York City Feb. 5-6. The primary goal of the workshop was to begin laying the groundwork for a nationwide entangled quantum Internet.

Building on the efforts of theChicago Quantum Exchangeat the University of Chicago, Argonne and Fermi National Laboratories, andLiQuIDNet(Long Island Quantum Distribution Network) at Brookhaven National Laboratory and Stony Brook University, the event was organized by Brookhaven. The technical program committee was co-chaired by Kerstin Kleese Van Dam, director of the Computational Science Initiative at Brookhaven, and Inder Monga, director of ESnet at Lawrence Berkeley National Lab.

The dollars we have put into quantum information science have increased by about fivefold over the last three years, Dabbar told the New York Timeson February 10 after the Trump Administration announced a new budget proposal that includes significant funding for quantum information science, including the quantum Internet.

In parallel with the growing interest and investment in creating viable quantum computing technologies, researchers believe that a quantum Internet could have a profound impact on a number of application areas critical to science, national security, and industry. Application areas include upscaling of quantum computing by helping connect distributed quantum computers, quantum sensing through a network of quantum telescopes, quantum metrology, and secure communications.

Toward this end, the workshop explored the specific research and engineering advances needed to build a quantum Internet in the near term, along with what is needed to move from todays limited local network experiments to a viable, secure quantum Internet.

This meeting was a great first step in identifying what will be needed to create a quantum Internet, said Monga, noting that ESnet engineers have been helping Brookhaven and Stony Brook researchers build the fiber infrastructure to test some of the initial devices and techniques that are expected to play a key role in enabling long-distance quantum communications. The group was very engaged and is looking to define a blueprint. They identified a clear research roadmap with many grand challenges and are cautiously optimistic on the timeframe to accomplish that vision.

Berkeley Labs Thomas Schenkel was the Labs point of contact for the workshop, a co-organizer, and co-chair of the quantum networking control hardware breakout session. ESnets Michael Blodgett also attended the workshop.

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Scientists discover how to use time crystals to power superconductors – Big Think

The concept of time crystals comes from the realm of counterintuitive mind-melding physics ideas that may actually turn out to have real-world applications. Now comes news that a paper proposes merging time crystals with topological superconductors for applications in error-free quantum computing, extremely precise timekeeping and more.

Time crystals were first proposed as hypothetical structures by the Nobel-Prize winning theoretical physicist Frank Wilczek and MIT physicists in 2012. The remarkable feature of time crystals is that they would would move without using energy. As such they would appear to break the fundamental physics law of time-translation symmetry. They would move while staying in their ground states, when they are at their lowest energy, appearing to be in a kind of perpetual motion. Wilczek offered mathematical proof that showed how atoms of crystallizing matter could regularly form repeating lattices in time, while not consuming or producing any energy.

Time crystals have since been experimentally created in various labs.

Now researchers at the California Institute of Technology (Caltech) and the Weizmann Institute in Israel found that theoretically you can create a system that combines time crystals with so-called topological superconductors.

The field of topology looks at the properties of objects that are unchangeable (or "invariant') despite deformations like stretching, twisting, or bending. In a topological insulator, the properties linked to the electron wave function would be considered topologically invariant.

As the scientists themselves explain, "Time crystals form when arbitrary physical states of a periodically driven system spontaneously break discrete time-translation symmetry." What the researchers noticed is that when they introduced "one-dimensional time-crystalline topological superconductors" they found a fascinating interaction where "time-translation symmetry breaking and topological physics intertwineyielding anomalous Floquet Majorana modes that are not possible in free-fermion systems."

Majorana fermions are particles that have their own anti-particles.

"Physicists Gil Refael and Jason Alicea explain the unique properties of electrons constrained to a 2 Dimensional world, and how they can be used to make noise-proof Quantum Computers."

The research was led by Jason Alicea and Aaron Chew from CalTech, as well as David Mross from the Weizmann Institute in Israel.

While studying Majorana fermions, the team observed that it is possible to enhance topological superconductors by coupling them to magnetic degrees of freedom that could be controlled. "Then we realized that by turning those magnetic degrees of freedom into a time crystal, topological superconductivity responds in remarkable ways," shared Alicea.

Aaron Chew (left) and David Mross (right).

Credit: Jason Alicea

One way the phenomen noticed by the scientists could be potentially exploited is to create more stable qubits - the bit of quantum information in quantum computing. The race to create qubits is at the threshold of bringing on a true quantum technology revolution, as writes Popular Mechanics.

"It's tempting to imagine generating some useful quantum operations by controlling the magnetic degrees of freedom that intertwine with the topological physics. Or perhaps certain noise channels can be suppressed by exploiting time crystals," said Alicea.

Check out their new paper in Physical Review Letters.

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Scientists discover how to use time crystals to power superconductors - Big Think

Quantum Computing Market With Four Main Geographies And Their Countries – Instant Tech News

This research study on Quantum Computing market reports offers the comparative assessment of Quantum Computing market and consist of Historical data, Significance, statistical data, size & share, Market Price & Demand, Business overview, Market Analysis By Product and Market Trends by Key Players. This Quantum Computing Market is Segmented in two type on the basis of type of materials and end-users. It has global market covered in all the regions, ranging to that fundamental market, key trends and segmentation analysis are coated throughout Quantum Computing market report.

Sales volume, Price (USD/Unit), revenue (Million USD) and market share coated by Key Players such Top Players are:

Wave Systems Corp, 1QB Information Technologies Inc, QC Ware, Corp, Google Inc, QxBranch LLC, Microsoft Corporation, International Business Machines Corporation, Huawei Technologies Co., Ltd, ID Quantique SA, and Atos SE.

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The analysts forecast the CAGR overall rate percentages of Global Quantum Computing Market to grow over the period 2020-2030. So this Quantum Computing Market report gives you Pre-planned Compound Annual rate of growth (CAGR) with different amount, During the Forecast Period, Market on Quantum Computing Report is estimated to register a CAGR of Definite value. Definitions, classifications, applications & Business overview, product specifications, manufacturing processes, cost structures, raw materials and requirement as per your choice also given by this Quantum Computing market Report.

Segmentation:

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This report additionally represents product specification, method and product cost structure. Production is separated by regions, technology and applications. Table, figure, charts, TOCs, chapters etc provided by Silicon-germanium Semiconductors industry. Crystal clear data to the client giving a brief details on Silicon-germanium Semiconductors markets and its trends. Silicon-germanium Semiconductors new project SWOT analysis, investment practicable business analysis, investment come analysis and development trend analysis. The rising opportunities of the fastest growing Silicon-germanium Semiconductors markets segments are covered throughout this report.

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Quantum Computing Market 2019 Analysis by Key Players, Share, Trend, Segmentation and Forecast to 2026 – Instant Tech News

Verified Market Research recently added a research report titled, Quantum Computing Market Size and Forecast to 2026. The research report represents the potential growth opportunities that prevail within the global market. The report is analyzed on the idea of secondary research methodologies acquired from historic and forecast data. The Quantum Computing market is expected to grow substantially and thrive in terms of volume and value during the forecast period. The report will provide an insight into the growth opportunities and restraints that construct the market. Readers can gain meaningful comprehension about the future of the market.

Global Quantum Computing Market was valued at USD 89.35 million in 2016 and is projected to reach USD 948.82 million by 2025, growing at a CAGR of 30.02% from 2017 to 2025.

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Top 10 Companies in the Quantum Computing Market Research Report:

QC Ware Corp., D-Wave Systems, Cambridge Quantum Computing, IBM Corporation, Magiq Technologies, Qxbranch, Research at Google Google, Rigetti Computing, Station Q Microsoft Corporation, 1qb Information Technologies

Competitive Landscape

The insightful research report on the Quantum Computing market includes Porters five forces analysis and SWOT analysis to understand the factors impacting consumer and supplier behavior. It helps the reader understand the strategies and collaborations that players are that specialize in combat competition within the market. The comprehensive report provides a big microscopic check out the market. The reader can identify the footprints of the manufacturers by knowing about the worldwide revenue of manufacturers, the worldwide price of manufacturers, and production by manufacturers during the forecast period of 2015 to 2019.

Global Quantum Computing Market: Drivers and Restraints

The report offers underlying drivers that compel the consumers to take a position within the products and services. The detailed information assists readers in understanding the requirements of consumer demands. The report provides drivers at the local and global levels to assist determine the economic process . This information will help readers decide potential strategies that can help them stay ahead in the competitive industry.

Restraints provided in this section of the report contrasts the drivers segment as it explains the factors that can hamper the growth of the Quantum Computing market during the forecast period. Restraints play a pivotal role in the global and regional market as it bends the prospective opportunities in the market. Readers can weigh and asses the drivers and restraints before making any investments or strategies.

Global Quantum Computing Market: Segment Analysis

The report includes major segments like product type and end-user that provide an array of components that determine the portfolio of the Quantum Computing industry. Each type furnishes information regarding the sales value during the forecast period. The understanding of the segment directs the readers in recognizing the importance of things that shape the market growth.

Global Quantum Computing Market: Regional Analysis

This section of the report provides detailed information about each region and how numerous factors of that particular region affect the growth of the Quantum Computing market. The government policies, weather, politics, and other factors determine the longer term of the market differently in each region. The major regions covered in the report include North America, Europe, Asia Pacific, the Middle East, and Africa, and others.

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Table of Content

1 Introduction of Quantum Computing Market

1.1 Overview of the Market1.2 Scope of Report1.3 Assumptions

2 Executive Summary

3 Research Methodology of Verified Market Research

3.1 Data Mining3.2 Validation3.3 Primary Interviews3.4 List of Data Sources

4 Quantum Computing Market Outlook

4.1 Overview4.2 Market Dynamics4.2.1 Drivers4.2.2 Restraints4.2.3 Opportunities4.3 Porters Five Force Model4.4 Value Chain Analysis

5 Quantum Computing Market, By Deployment Model

5.1 Overview

6 Quantum Computing Market, By Solution

6.1 Overview

7 Quantum Computing Market, By Vertical

7.1 Overview

8 Quantum Computing Market, By Geography

8.1 Overview8.2 North America8.2.1 U.S.8.2.2 Canada8.2.3 Mexico8.3 Europe8.3.1 Germany8.3.2 U.K.8.3.3 France8.3.4 Rest of Europe8.4 Asia Pacific8.4.1 China8.4.2 Japan8.4.3 India8.4.4 Rest of Asia Pacific8.5 Rest of the World8.5.1 Latin America8.5.2 Middle East

9 Quantum Computing Market Competitive Landscape

9.1 Overview9.2 Company Market Ranking9.3 Key Development Strategies

10 Company Profiles

10.1.1 Overview10.1.2 Financial Performance10.1.3 Product Outlook10.1.4 Key Developments

11 Appendix

11.1 Related Research

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Quantum Computing Market 2019 Analysis by Key Players, Share, Trend, Segmentation and Forecast to 2026 - Instant Tech News

Googles Machine Learning Is Making You More Effective In 2020 – Forbes

The collection of web-based software that Google offers to businesses and consumers is officially known as G Suite. Most people are familiar with Gmail and Google Docs, but quite a few do not realize that they offer a whole range of productivity and collaboration tools via your computer or mobile device.

HONG KONG, HONG KONG - November 27: A woman using an Macbook Pro as she uses Google G Suite on ... [+] November 27, 2017 in Hong Kong, Hong Kong. (Photo by studioEAST/Getty Images)

I have been working on another post about consumer-level uses of artificial intelligence (AI), not the media-hyped creepiness, but the practical, useful ways that AI is helping us do more and be more. Google started me thinking about this as I have watched it add various smart functions (think AI) to email as well as increasing ways to help me complete or enhance a document, spreadsheet, or presentation with the Explore function.It keeps learning from you and adjusting to you with these features.

Draft and send email responses quicker: Two relatively new, intelligent features include Smart Compose and Smart Reply. Gmail will suggest ways to complete your sentences while drafting an email and suggest responses to incoming messages as one-click buttons (at the bottom of the newly received message). This works in relatively simple messages that are calling for answers like these:

Enable Smart Compose and Smart Reply by going to Settings (that little gear icon in the upper right of your email inbox). Smart Reply is automatically enabled when users switch to the new Gmail.

On mobile and desktop or web, Smart Reply utilizes machine learning to give you better responses the more you use it. So if you're more of a thanks! than a thanks. person, itll suggest the response that is more authentic to you. Subtle difference, for sure, but I have noticed with certain people I interact with, the punctuation does change to show more emotion. I have not seen any emojis popping up, however. That may be a good thing.

For some of the newest features, you must go to Settings, then click Experimental Access. Features that are under test have a special little chemistry bottle icon or emoji. Most of the features in this post have already been fully tested and released to the general public.

Auto-reminders to respond: Gmails new Nudging function reportedly will now automatically bump both incoming and outgoing messages to the top of your inbox after a few days if neither party has responded. You can turn this feature on/off in Settings. However, I have not had this work properly, but maybe I am simply too efficient. Not. Either way, I have not noticed these reminders yet.

Machine Learning in Google Docs, Google Sheets, and Google Slides

The Explore button in the lower right corner of Docs, Sheets, or Slides is machine learning (ML) in action. You can visualize data in Sheets without using a formula. The explore button is a compass-looking type star and as you hover over it, it expands. Once clicked, it serves as a search tool within these products.

Explore and visualize in Sheets to help you decipher data easily by asking Explore with words, not formulas to get answer about your data. You can ask it a question like, how many units were sold on Black Friday, or what is my best selling product? or how much was spent on payroll last month, can be asked directly instead of creating formulas to get an answer. Explore in Sheets is available on the web, Android and iOS. On Android, you click the three vertical dots to get to the menu and then Explore is listed. When you first click it, it offers a try an example option and creates a new spreadsheet showcasing various examples.

Explore in Docs gives you a way to stay focused in the same tab. Using Explore, you get a little sidebar with Web, Images, and Drive results. It provides instant suggestions based on the content in your document including related topics to learn about, images to insert, or more content to check out in Docs. You can also find a related document from Drive or search Google right within Explore. Explore in Docs is available in a web browser, but I did not find it on my mobile apps for Android or iOS.

Explore in Slides makes designing a presentation simple. I think theres some AI/ML going on here, as Explore dynamically generates design suggestions, based on the content of your slides. Then, pick a recommendation and apply it with a single click, without having to crop, resize or reformat.

Are all of these features going to single-handedly make you the most productive person on the planet? No, but they are definitely small and constant improvements that point the way to a more customized and helpful use of artificial intelligence and machine learning.

If you are looking for other creative ways that people and organizations are using G Suite, there are tons of great customer stories that Google shares about how big and small organizations and companies use its free and enterprise-level products that may give you ideas for how you can leverage their cloud software. I find many of these case studies inspiring, but that is based on how organizations are responding to community needs.

Check out this one from Eagle County, Colorado during a wildfire there and this one from the City of Los Angeles with a real-time sheet to show police officers which homeless shelters have available beds.

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Googles Machine Learning Is Making You More Effective In 2020 - Forbes

Fiddler Labs, SRI and Berkeley experts open up the black box of machine learning at TC Sessions: Robotics+AI – TechCrunch

As AI permeates the home, work, and public life, its increasingly important to be able to understand why and how it makes its decisions. Explainable AI isnt just a matter of hitting a switch, though; Experts from UC Berkeley, SRI, and Fiddler Labs will discuss how we should go about it on stage at TC Sessions: Robotics+AI on March 3.

What does explainability really mean? Do we need to start from scratch? How do we avoid exposing proprietary data and methods? Will there be a performance hit? Whose responsibility will it be, and who will ensure it is done properly?

On our panel addressing these questions and more will be two experts, one each from academia and private industry.

Trevor Darrell is a professor at Berkeleys Computer Science department who helps lead many of the universitys AI-related labs and projects, especially those concerned with the next generation of smart transportation. His research group focuses on perception and human-AI interaction, and he previously led a computer vision group at MIT.

Krishna Gade has passed in his time through Facebook, Pinterest, Twitter and Microsoft, and has seen firsthand how AI is developed privately and how biases and flawed processes can lead to troubling results. He co-founded Fiddler as an effort to address problems of fairness and transparency by providing an explainable AI framework for enterprise.

Moderating and taking part in the discussion will be SRI Internationals Karen Myers, director of the research outfits Artificial Intelligence Center and an AI developer herself focused on collaboration, automation, and multi-agent systems.

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Fiddler Labs, SRI and Berkeley experts open up the black box of machine learning at TC Sessions: Robotics+AI - TechCrunch

Mind Analytics creates the first technology platform for the travel industry that combines AI, Machine Learning and Big Data – Travel Daily News…

BARCELONA - Mind Analytics, a Spanish start-up specialising in data analytics to optimise decision making in the tourism industry, has launched the first tool in the travel industry that combines AI, Machine Learning and Big Data, designed to improve the conversion of hotel distribution wholesalers. The Travel Intelligence Engine (Travel/ie) solution captures, processes and analyses data in real time and uses that knowledge to improve distribution, detect errors and behaviour patterns, in order to improve the distribution of available product inventory and adapt offers to your customers.

This is technology developed in Spain which combines the advantages of advanced descriptive analytics, artificial intelligence and automated learning. The combination of these three functionalities allows you to better understand the tourism market and give an immediate response, optimising conversion by up to 30%.

Thanks to the analysis of customers and supplier data in real time, Travel/ie is a powerful tool to optimise the management of products offered by wholesalers. For example, it allows you to know the most requested destinations and dates, analyse the remaining rooms and, in turn, measure the infrastructure performance in detail or even detect integration and data mapping errors through an alarm system.

In this way, distributors can identify when a product is not being displayed correctly, detect a problem with a customer's request for a reservation, even a failure in network performance, and act immediately to avoid losing revenue.

For Joaquin Orono, CEO of the company, "Decisions based on real data are key to addressing the challenges of the tourism industry. Up to now, this process of analysis and interpretation of the data offered by Travel Intelligence Engine was done manually, an inefficient practice in terms of resource consumption that also generates errors. Therefore, we wanted to develop a state-of-the-art technological product that was the lever that companies in the tourism industry needed to optimise their profitability."

Mind Analytics has developed Travel/ie so wholesalers such as bed banks can manage large volumes of data. However, the data intelligence platform is expected to diversify to other segments of the travel industry such as hotels, travel agencies, car rental companies and airlines.

Integration with the company systemThe implementation of Travel/ie is carried out in a short time frame and does not affect each distributor's individual platform. Therefore, it integrates naturally with the system. First, the information relevant to the company is identified and a data collector is set up. Travel/ie obtains only the data necessary to optimise the business and does so in a non-invasive way, so that a panel adapted to the needs of the company is created.

To develop the integration, comparison and analysis of data, Travel/ie uses market leading technologies such as Google Cloud and Looker.

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Mind Analytics creates the first technology platform for the travel industry that combines AI, Machine Learning and Big Data - Travel Daily News...

Machine Learning In Medicine Market Global Business Insights and Development Analysis to 2026 – Instant Tech News

Global Machine Learning In Medicine Market Insights, Forecast to 2026:

The globalMachine Learning In Medicine Marketresearch report is a valuable source of insightful data for business strategists. It provides the industry overview with growth analysis and historical & futuristic cost, revenue, demand and supply data (as applicable). The research analysts provide an elaborate description of the value chain and its distributor analysis. This Market study provides comprehensive data which enhances the understanding, scope and application of this report.

The global Machine Learning In Medicine Market Analysis Report includesTop Companies:Google, Bio Beats, Jvion, Lumiata, DreaMed, Healint, Arterys, Atomwise, Health Fidelity, Gingeralong with their company profile, growth aspects, opportunities, and threats to the market development. This report presents the industry analysis for the forecast timescale. An up-to-date industry details related to industry events, import/export scenario, market share is covered in this report.

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Global Machine Learning In Medicine Market Split by Product Type and Applications:

This report segments the Global Machine Learning In Medicine Market on the basis ofTypesare:

Supervised Learning

Unsupervised Learning

Semi Supervised Learning

Reinforced Leaning

On the basis ofApplication, the Global Machine Learning In Medicine Market is segmented into:

Diagnosis

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Machine Learning In Medicine Market Global Business Insights and Development Analysis to 2026 - Instant Tech News

‘Technology is never neutral’: why we should remain wary of machine learning in children’s social care – Communitycare.co.uk

(credit: Pablo Lagarto / Adobe Stock)

On 1 February 2020, YouTuber Simon Weekert posted a video on YouTube claiming to have redirected traffic by faking traffic jams on Google Maps. The video shows Weekert walking slowly along traffic-free streets in Berlin, pulling a pile of second-hand mobile phones in a cart behind him and Google Maps generating traffic jam alerts because the phones had their location services turned on.

Weekerts performance act demonstrates the fragility and vulnerability of our systems and their difficulty in interpreting outliers, and highlights a kind of decisional blindness when we think of data as objective, unambiguous and interpretation free, as he put it. There are many other examples of decisional blindness relating to drivers following Google Maps and falling off cliffs or driving into rivers.

Google has the resources, expertise and technology to rapidly learn from this experience and make changes to avoid similar situations. But the same vulnerability to hacking or outliers applies to the use of machine learning in childrens social care (CSC) and this raises the question of whether the sector has the means to identity and rectify issues in a timely manner and without adverse effects for service users.

Have you ever had the experience of asking the wrong question in Google search and getting the right answer? Thats because of contextual computing that makes use of AI and machine learning.

At its heart, machine learning is the application of statistical techniques to identify patterns and enable computers to use data to progressively learn and improve their performance.

From Google search and Alexa to online shopping, and from games and health apps to WhatsApp and online dating, most online interactions are mediated by AI and machine learning. Like electricity, AI and machine learning will power every software and digital device and will transform and mediate every aspect of human experience mostly without end users giving them a thought.

But there are particular concerns about their applications in CSC and, therefore, a corresponding need for national standards for machine learning in social care and for greater transparency and scrutiny around the purpose, design, development, use, operation and ethics of machine learning in CSC. This was set out in What Works for Childrens Social Cares ethics review into machine learning, published at the end of January.

The quality of machine learning systems predictive analysis is dependent on the quality, completeness and representativeness of the dataset they draw on. But peoples lives are complex, and often case notes do not capture this complexity and instead are complemented by practitioners intuition and practice wisdom. Such data lacks the quality and structure needed for machine learning applications, making high levels of accuracy harder to achieve.

Inaccuracy in identifying children and families can result in either false positives that infringe on peoples rights and privacy, cause stress and waste time and resources, or false negatives that miss children and families in need of support and protection.

Advocates of machine learning often point out that systems only provide assistance and recommendations, and that it remains the professionals who make actual decisions. Yet decisional blindness can undermine critical thinking, and false positives and negatives can result in poor practice and stigmatisation, and can further exclusion, harm and inequality.

Its true that AI and machine learning can be used in empowering ways to support services or to challenge discrimination and bias. The use of Amazons Alexa to support service users in adult social care is, while not completely free of concerns, one example of positive application of AI in practice.

Another is Essex councils use of machine learning to produce anonymised aggregate data at community level of children who may not be ready for school by their fifth birthday. This data is then shared with parents and services who are part of the project to inform their funding allocation or changes to practice as need be. This is a case of predictive analytics being used in a way that is supportive of children and empowering for parents and professionals.

The Principal Children and Families Social Worker (PCFSW) Network is conducting a survey of practitioners to understand their current use of technology and challenges and the skills, capabilities and support that they need.

It only takes 10 minutes to complete the survey on digital professionalism and online safeguarding. Your responses will inform best practice and better support for social workers and social care practitioners to help ensure practitioners lead the changes in technology rather than technology driving practice and shaping practitioners professional identity.

But its more difficult to make such an assessment in relation to applications that use hundreds of thousands of peoples data, without their consent, to predict child abuse. While there are obvious practical challenges around seeking the permission of huge numbers of people, failing to do so shifts the boundaries of individual rights and privacy vis--vis surveillance and the power of public authorities. Unfortunately though, ethical concerns do not always influence the direction or speed of change.

Another controversial recent application of technology is the use of live facial recognition cameras in London. An independent report by Essex Universitylast year suggested concerns with inaccuracies in use of live facial recognition, while the Met Polices senior technologist, Johanna Morley said millions of pounds would need to be invested in purging police suspect lists and aligning front- and back-office systems to ensure the legality of facial recognition cameras. Despite these concerns, the Met will begin using facial recognition cameras in London streets, with the aim of tackling serious crime, including child sexual exploitation.

Research published in November 2015, meanwhile, showed that a flock of trained pigeons can spot cancer in images of biopsied tissue with 99% accuracy; that is comparable to what would be expected of a pathologist. At the time, one of the co-authors of the report suggested that the birds might be able to assess the quality of new imaging techniques or methods of processing and displaying images without forcing humans to spend hours or days doing detailed comparisons.

Although there are obvious cost efficiencies in recruiting pigeons instead of humans, I am sure most of us will not be too comfortable having a flock of pigeons as our pathologist or radiologist.

Many people would also argue more broadly that fiscal policy should not undermine peoples health and wellbeing. Yet the past decade of austerity, with 16bn in cuts in core government funding for local authorities by this year and a continued emphasis on doing more with less, has led to resource-led practices that are far from the aspirations of Children Act 1989 and of every child having the opportunity to achieve their potential.

Technology is never neutral and there are winners and losers in every change. Given the profound implications of AI and machine learning for CSC, it is essential such systems are accompanied by appropriate safeguards and processes that prevent and mitigate false positives and negatives and their adverse impact and repercussions. But in an environment of severe cost constraints, positive aspirations might not be matched with adequate funding to ensure effective prevention and adequate support for those negatively impacted by such technologies.

In spite of the recent ethics reviews laudable aspirations, there is also the real risk that many of the applications of machine learning pursued to date in CSC may cement current practice challenges by hard-coding austerity and current thresholds into systems and the future of services.

The US constitution was written and ratified by middle-aged white men and it took over 130 years for women to gain the right of suffrage and 176 years to recognise and outlaw discrimination based on race, sex, religion and national origin. Learning from history would suggest we must be cautious about reflecting childrens social cares operating context into systems, all designed, developed and implemented by experts and programmers who may not represent the diversity of the people who will be most affected by such systems.

Dr Peter Buzzi (@MHChat) is the director of Research and Management Consultancy Centre and the Safeguarding Research Institute. He is also the national research lead for the Principal Children and Families Social Worker (PCFSW) Networks online safeguarding research and practice development project.

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'Technology is never neutral': why we should remain wary of machine learning in children's social care - Communitycare.co.uk