Hide and Hedonism invite guests to a virtual wine dinner – Spear’s WMS

The Mayfair restaurant and wine boutique invite diners to a virtual four-course dinner with head sommelier Julien Sarrasin

Guests of Mayfair restaurant Hide can now enjoy a four-course menu prepared by Ollie Dabbous and paired with fine wines from Hedonism all in the comfort of their own home.

The Michelin-starred restaurant and its sister wine merchant Hedonism have upped the ante in the high-end food delivery stakes by offering diners the chance to join HIDE at HOMEs head sommelier Julien Sarrasin by live video feed on Wednesday 13th May.

Sarrasin will introduce each dish and discuss each of the wines in turn. Diners will learn about the winemaker, the grape varieties and why each fine wine was selected to pair with a particular course.

The menu starts with a chilled pine broth amuse bouche with strawberries, avocado, basil and pistachio. The starter is scallop tartare with Exmoor caviar, followed by Champagne-poached cornfed chicken, sptzle and black truffle. Dessert is a baked Alaska made with cascara, coffee and pecan.

Wines will be delivered one or two days before the date of the event. The meal will be delivered on the evening of Wednesday 13th May. The offer is limited to a list of London postcodes which is available on the HIDE at HOME site.

The menu will be prepared by Ollie Dabbous and his team at Hide. The chef opened his first restaurant, Dabbous, in 2012 and earned his first Michelin star. After closing Dabbous in 2017, the chef joined forces with Hedonism Wines to launch Hide, which earned a Michelin star within six months of opening in 2018.

The virtual wine dinner from HIDE at HOME is just one in a series of virtual events being run by the restaurant. Others include a spirit tasting to discover the smoky drams of Scotland with specialist Tom Olive, a winemaker tasting with the Chef de Cave at Charles Heidsieck, Cyril Brun, and a whisky tasting with The Macallans owner David Sinclair.

Web: hide.co.uk/home

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Hide and Hedonism invite guests to a virtual wine dinner - Spear's WMS

Budget realities from COVID-19 cut $40.6 million from Prince William budgets – Inside NoVA

Prince William County will leave the rate on real estate taxes unchanged for the next fiscal year, as the budget reality of the COVID-19 pandemic forced supervisors to ditch new spending and a proposed tax rate increase.

More than 25 Prince William County residents who talked remotely to the board of county supervisors Tuesday were divided with many supporting a proposal to keep the real estate tax rate steady in an effort to fund schools and social services, while others said the county should decrease the tax rate as thousands in the county have lost jobs and others face furloughs and an uncertain future amid the COVID-19 pandemic.

The board adopted its $1.09 billion budget for fiscal year 2021 that starts July 1.

The board adopted the same real estate tax rate as this fiscal year: $1.125 for every $100 of assessed value. That doesnt mean that taxes wont go up. Residents who have seen property values climb will see an increase in their tax bill, about a $177 increase on the average residential tax bill.

The vote was 5-3. Supervisors Jeanine Lawson, R-Brentsville, Pete Candland, R-Gainesville, and Yesli Vega, R-Coles, argued for a tax rate of $1.085 per every $100 of value. Democratic supervisors argued the cuts would limit county services.

Supervisors also voted to increase the tax rate on computer equipment by 10 cents to $1.35 per every $100 of assessed value a charge that primarily affects data centers.

The adopted budget has $40.6 million less than staff had proposed on Feb. 18. Of that money, $22.7 million was expected to be added to school division revenue.

The board ended up allocating $629.6 million to the division, including $625.3 million that is part of the countys revenue sharing agreement, along with additional funding for class size reduction, costs related to the 13th high school and more.

Chair Ann Wheeler said she expects the board will have to revisit its budget every quarter due to the uncertainty of the pandemic.

When the board adopted its current budget last year, Republicans held a 6-2 majority. Now, Democrats hold a 5-3 majority.

The budget included $7 million for pay increases for about 1,500 employees, raises that were recommended to the board to ensure employees are being paid equally for similar work and to make sure pay is competitive with other governments in the region.

Supervisor Andrea Baileys proposal to add an additional $150,000 for community partners nonprofits such as ACTS in Dumfries to the existing proposal of $92,904 for fiscal year 2021 was also approved.

David Sinclair, the countys director of the office of management and budget, told the board the fiscal 2021 budget means about $36 million less than the school board adopted as part of its recommended budget. The school board is waiting to adjust its spending plan until after the county and the state determine how deep cuts will go.

On Feb. 18, County Executive Christopher Martino proposed a budget based on a 2-cent increase to the real estate tax rate. After the coronavirus, Wheeler sought a budget that kept the tax rate unchanged.

Among items cut was a 3% merit raise for county staff. Martino also has implemented a hiring freeze unless the position is required for public safety related to the pandemic, focused spending on core services, and postponed large construction projects that are not under contract, among other measures.

Staff project the county will see $14.2 million less in revenue in fiscal year 2021. The county also expects to receive $2.4 million less in revenue prior to June 30, when fiscal 2020 ends.

In Prince William Health District, which includes the county, Manassas and Manassas Park, reported 1,677 people who tested positive for COVID-19, 183 people were hospitalized and 23 people have died due to the virus, according to the Virginia Department of Health.

According to the Virginia Employment Commission, over 28,600 Prince William residents filed for unemployment benefits between March 15 and April 18.

The board also approved the request from Supervisor Andrea Bailey, D-Potomac, for $2 million for the environmental and preliminary design for the Van Buren Road extension project. That funding is coming from the Northern Virginia Transportation Authority.

Supervisor Margaret Franklin, D-Woodbridge, proposed on April 21 to starta small business program so the county can offer $10,000 loans and grants through its Industrial Development Authority. This week,theboard approved $1 million for that program starting this fiscal year 2020 and carrying over any funds to fiscalyear 2021. Also starting this fiscal year, is a program pitched by Franklin to dedicate $500,000 to offer rental or utility assistance for people who are low income or elderly or disabled.

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Budget realities from COVID-19 cut $40.6 million from Prince William budgets - Inside NoVA

Online course trains students in the bizarre world of quantum computing – Livescience.com

When the bizarre world of quantum physics where a "cat" can be both alive and dead, and particles a galaxy apart are connected is merged with computer technology, the result is unprecedented power to anyone who masters this technology first.

There is an obvious dark side. Imagine a world where online bank accounts could be easily hacked into and robbed. But this power can also be turned to good, allowing new drugs to be designed with unprecedented speed to cure disease. To prepare for such a future, many countries are investing billions to unlock the potential of what is called quantum computing. With an eye toward the future, a group of researchers at Fermilab,a particle physics laboratory in Batavia, Ill., has worked with high-school teachers to develop a program to train their students in this emerging field.

This program, called "Quantum Computing as a High School Module," was developed in collaboration with young students in mind. But it's also a perfect diversion for science enthusiasts of any age who suddenly have a lot of time on their hands.

This online training course introduces students to quantum concepts, including superposition, qubits, encryption, and many others. These additional concepts include quantum measurement, entanglement and teleportation; students will also learn and how to use quantum computers to prevent hacking. The course is also appropriate for community college or undergraduate students in areas outside of physics, such as computer science, engineering or mathematics, as well as a science literate public. One of the course's teachers, Ranbel Sun wrote, "It was great to work with a couple of America's smartest researchers to make sure that the science was right. Combining their knowledge and our teaching experience, we have developed an understandable learning program which bridges the gap between popular media and college textbooks."

Related: 12 stunning quantum physics experiments

Quantum computing uses the principles of quantum physics, which were developed in the early 1900s. Quantum physics describes the tiny realm of atoms, where the laws of nature seem to be very different from the world we can see. In this microcosm, electrons and particles of light called photons simultaneously act as both waves and particles a seeming absurdity, but one that is well accepted among scientists.

This non-intuitive quantum behavior has been exploited to develop powerful technologies, like the lasers and transistors that form the backbone of our technological society. Nobel Prize winning physicist Richard Feynman was the first to suggest that computers could be built to directly exploit the laws of quantum mechanics. If successful, these quantum computers could solve incredibly important and difficult problems that are too complex for even the most powerful modern supercomputers to solve. Last year, Google used a quantum computer called Sycamore to solve a problem thought to be virtually unsolvable by conventional computers; a calculation that would take the most powerful supercomputers 10,000 years to finish was solved in just 200 seconds by Sycamore.

The familiar computer on your desk uses a vast array of objects called bits to operate. Bits are basically simple switches that can be either on or off, which is mathematically equivalent to ones and zeros. Quantum computers rely on qubits, which can simultaneously be both on and off at the same time. This peculiar feature is common in the quantum world and is called superposition: being in two states at once. Researcher Ciaran Hughes said, "The quantum world is very different from the familiar one, which leads to opportunities not available using classical computers."

In 1994, Peter Shor invented an algorithm that revealed the power of quantum computing. His algorithm would allow quantum computers to factorize a number enormously faster than any classically known algorithm. Factorizing numbers is important because the encryption system used by computers to communicate securely relies on the mathematics of prime numbers. Prime numbers are numbers that are divisible only by one and themselves.

In a standard encryption algorithm, two very large prime numbers are multiplied together, resulting in an even larger number. The key to breaking the security code is to take the large number and find the two prime numbers that were multiplied together to make it. Finding these prime numbers is extremely hard for ordinary computers and can take centuries to accomplish.

However, using Shor's quantum algorithm, finding these prime factors is much easier. A working quantum computer would make our standard method of encryption no longer secure, resulting in the need for new encryption methods. Fermilab researcher Jessica Turner said, "Quantum computing is a very new way of thinking and will be revolutionary, but only if we can develop programmers with quantum intuition."

Obviously, any nation state or individual who is able to crack encryption codes will have a huge information advantage. The competition to develop working quantum computers is the new space race.

Quantum computing has the potential to overturn how computers securely communicate: from health care, to financial services and online security. Like it or not, the future is quantum computing. To fully reap the rewards of this quantum revolution requires a quantum fluent workforce. This new program is a very helpful step towards that goal.

The researchers have made their training program freely available.

Originally published on Live Science.

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Online course trains students in the bizarre world of quantum computing - Livescience.com

When quantum computing and AI collide – Raconteur

Machine-learning and quantum computing are two technologies that have incredible potential in their own right. Now researchers are bringing them together. The main goal is to achieve a so-called quantum advantage, where complex algorithms can be calculated significantly faster than with the best classical computer. This would be a game-changer in the field of AI.

Such a breakthrough could lead to new drug discoveries, advances in chemistry, as well as better data science, weather predictions and natural-language processing. We could be as little as three years away from achieving a quantum advantage in AI if the largest players in the quantum computing space meet their goals, says Ilyas Khan, chief executive of Cambridge Quantum Computing.

This comes after Google announced late last year that it had achieved quantum supremacy, claiming their quantum computer had cracked a problem that would take even the fastest conventional machine thousands of years to solve.

Developing quantum machine-learning algorithms could allow us to solve complex problems much more quickly. To realise the full potential of quantum computing for AI, we need to increase the number of qubits that make up these systems, says Dr Jay Gambetta, vice president of quantum computing at IBM Research.

Quantum devices exploit the strange properties of quantum physics and mechanics to speed up calculations. Classical computers store data in bits, as zeros or ones. Quantum computers use qubits, where data can exist in two different states simultaneously. This gives them more computational fire power. Were talking up to a million times faster than some classical computers.

And when you add a single qubit, you double the quantum computers processing power. To meet Moores Law [the number of transistors on a computer chip is doubled about every two years while the cost falls], you would need to add a single qubit every year, says Peter Chapman, chief executive of IonQ.

Our goal is to double the number of qubits every year. We expect quantum computers to be able to routinely solve problems that supercomputers cannot, within two years.

Already industrial behemoths, such as IBM, Honeywell, Google, Microsoft and Amazon, are active in the quantum computing sector. Their investments will have a major impact on acceleratingdevelopments.

We expect algorithm development to accelerate considerably. The quantum community has recognised economic opportunities in solving complex optimisation problems that permeate many aspects of the business world. These range from how do you assemble a Boeing 777 with millions of parts in the correct order? to challenges in resource distribution, explains Dr David Awschalom, professor of quantum information at the University of Chicago.

The quantum community has recognised economic opportunities in solving complex optimisation problems that permeate many aspects of the business world

Many of the computational tasks that underlie machine-learning, used currently for everything from image recognition to spam detection, have the correct form to allow a quantum speed up. Not only would this lead to faster calculations and more resource-efficient algorithms, it could also allow AI to tackle problems that are currently unfeasible because of their complexity and size.

Quantum computers arent a panacea for all humankinds informatic problems. They are best suited to very specific tasks, where there are a huge number of variables and permutations, such as calculating the best delivery route for rubbish trucks or the optimal path through traffic congestion. Mitsubishi in Japan and Volkswagen in Germany have deployed quantum computing with AI to explore solutions to these issues.

There will come a time when quantum AI could be used to help us with meaningful tasks from industrial scheduling to logistics. Financial optimisation for portfolio management could also be routinely handled by quantum computers.

This sounds like it might have limited use, but it turns out that many business problems can be expressed as an optimisation problem. This includes machine-learning problems, says Chapman.

Within a few short years we will enter the start of the quantum era. Its important for people to be excited about quantum computing; it allows government funding to increase and aids in recruitment. We need to continue to push the technology and also to support early adopters to explore how they can apply quantum computing to their businesses.

However, its still early days. The next decade is a more accurate time frame in terms of seeing quantum computing and AI coalesce and really make a difference. The need to scale to larger and more complex problems with real-world impact is one area of innovation, as is creating quantum computers that have greater precision and performance.

The limitation of quantum technology, particularly when it comes to AI, is summarised by the term decoherence. This is caused by vibrations, changes in temperature, noise and interfacing with the external environment. This causes computers to lose their quantum state and prevents them from completing computational tasks in a timely manner or at all, says Khan.

The industrys immediate priority has shifted from sheer processing power, measured by qubits, to performance, better measured by quantum volume. Rightly so the industry is channelling its energy into reducing errors to break down this major barrier and unlock the true power of machine-learning.

Over time it is the ease of access to these computers that will lead to impactful business applications and the development of successful quantum machine-learning. IBM has opened its doors to its quantum computers via the cloud since 2016 for anyone to test ideas. In the process it has fostered a vibrant community with more than 200,000 users from over 100 organisations.

The more developers and companies that get involved in first solving optimisation problems related to AI and then over time building quantum machine-learning and AI development, the sooner well see even more scalable and robust applications with business value, explains Murray Thom, vice president of software at D-Wave Systems.

Most importantly, we need a greater number of smart people identifying and developing applications. That way we will be able to overcome limitations much faster, and expand the tools and platform so they are easier to use. Bringing in more startups and forward-thinking enterprise organisations to step into quantum computing and identify potential applications for their fields is also crucial.

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Devs: Here’s the real science behind the quantum computing TV show – New Scientist News

By Rowan Hooper

BBC/FX Networks

TVDevsBBC iPlayer and FX on Hulu

Halfway through episode two of Devs, there is a scene that caused me first to gasp, and then to swear out loud. A genuine WTF moment. If this is what I think it is, I thought, it is breathtakingly audacious. And so it turns out. The show is intelligent, beautiful and ambitious, and to aid in your viewing pleasure, this spoiler-free review introduces some of the cool science it explores.

Alex Garlands eight-part seriesopens with protagonists Lilyand Sergei, who live in a gorgeous apartment in San Francisco. Like their real-world counterparts, people who work atFacebook orGoogle, the pair take the shuttle bus to work.

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They work at Amaya, a powerful but secretive technology company hidden among the redwoods. Looming over the trees is a massive, creepy statue of a girl: the Amaya the company is named for.

We see the company tag line asLily and Sergei get off the bus: Your quantum future. Is it just athrow-away tag, or should we think about what that line means more precisely?

Sergei, we learn, works on artificial intelligence algorithms. At the start of the show, he gets some time with the boss, Forest, todemonstrate the project he has been working on. He has managed to model the behaviour of a nematode worm. His team has simulated the worm by recreating all 302 of its neurons and digitally wiring them up. This is basically the WormBot project, an attempt to recreate a life form completely in digital code. The complete map of the connections between the 302 neurons of the nematode waspublished in 2019.

We dont yet have the processing power to recreate theseconnections dynamically in a computer, but when we do, it will be interesting to consider if the resulting digital worm, a complete replica of an organic creature, should be considered alive.

We dont know if Sergeis simulation is alive, but it is so good, he can accurately predict the behaviour of the organic original, a real worm it is apparently simulating, up to 10 seconds in thefuture. This is what I like about Garlands stuff: the show has only just started and we have already got some really deep questions about scientific research that is actually happening.

Sergei then invokes the many-worlds interpretation of quantum mechanics conceived by Hugh Everett. Although Forest dismisses this idea, it is worth getting yourhead around it because the show comes back to it. Adherents say that the maths of quantum physics means the universe isrepeatedly splitting into different versions, creating a vast multiverse of possible outcomes.

At the core of Amaya is the ultrasecretive section where thedevelopers work. No one outside the devs team knows what it is developing, but we suspect it must be something with quantum computers. I wondered whether the devssection is trying to do with the 86 billion neurons of thehuman brain what Sergei has been doing with the 302 neurons of the nematode.

We start to find out when Sergei is selected for a role in devs. He must first pass a vetting process (he is asked if he is religious, a question that makes sense later) and then he is granted access to the devs compound sealed by alead Faraday cage, gold mesh andan unbroken vacuum.

Inside is a quantum computer more powerful than any currently in existence. How many qubits does it run, asks Sergei, looking inawe at the thing (it is beautiful, abit like the machines being developed by Google and IBM). Anumber that it is meaningless to state, says Forest. As a reference point, the best quantum computers currently manage around 50 qubits, or quantum bits. We can only assume that Forest has solved the problem ofdecoherence when external interference such as heat or electromagnetic fields cause qubits to lose their quantum properties and created a quantum computer with fantasticprocessing power.

So what are the devs using it for? Sergei is asked to guess, and then left to work it out for himself from gazing at the code. He figures it out before we do. Then comes that WTF moment. To say any more will give away the surprise. Yet as someone remarks, the world is deterministic, but with this machine we are gaining magical powers. Devs has its flaws, but it is energising and exciting to see TV this thoughtful: it cast a spell on me.

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Archer in trading halt pending material agreement over quantum computing tech – Stockhead

Super diversified quantum computing/health tech/battery metals play ArcherMaterials (ASX:AXE) is in a trading halt as it finalises a material agreement over its 12CQ quantum computing chip technology.

Globally, the race is on to develop quantum computers, which will operate at speeds eclipsing that of classic computers.

The nascent, rapidly growing quantum computing sector has the potential to impact a lot of sectors, offering potential solutions to complex computation, cryptography and simulation problems.

In late 2019, Tractica predicted that total quantum computing market revenue will reach $US9.1 billion ($14.06 billion) annually by 2030, up from $US111.6 million in 2018.

READ: What the heck is quantum computing and is it worth investing in?

But data is stored in qubits (like a classical computers data is stored in bits), and many quantum computers require their qubits to be cooled to nearly absolute zero to prevent errors occurring.

This is where Archers tech comes in it is developing a quantum computer chip that, if successful, will allow quantum computers to be mobile and operate at room temperature.

During the March quarter, Archer kicked off the next stage of the development of its 12CQ project focussed on completing the quantum measurements required to build a working chip prototype.

Archer will remain in trading halt until the earlier of the material announcement to the market, or the commencement of trade on Tuesday, 5 May.

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Announcing the IBM Quantum Challenge – Quantaneo, the Quantum Computing Source

Today, we have 18 quantum systems and counting available to our clients and community. Over 200,000 users, including more than 100 IBM Q Network client partners, have joined us to conduct fundamental research on quantum information science, develop the applications of quantum computing in various industries, and educate the future quantum workforce. Additionally, 175 billion quantum circuits have been executed using our hardware, resulting in more than 200 publications by researchers around the world.

In addition to developing quantum hardware, we have also been driving the development of powerful open source quantum software. Qiskit, written primarily in Python, has grown to be a popular quantum computing software development kit with several novel features, many of which were contributed by dedicated Qiskitters.

Thank you to everyone who has joined us on this exciting journey building the largest and most diverse global quantum computing community.

The IBM Quantum Challenge As we approach the fourth anniversary of the IBM Quantum Experience, we invite you to celebrate with us by completing a challenge with four exercises. Whether you are already a member of the community, or this challenge is your first quantum experiment, these four exercises will improve your understanding of quantum circuits. We hope you also have fun as you put your skills to test.

The IBM Quantum Challenge begins at 9:00 a.m. US Eastern on May 4, and ends 8:59:59 a.m. US Eastern on May 8. To take the challenge, visit https://quantum-computing.ibm.com/challenges.

In recognition of everyones participation, we are awarding digital badges and providing additional sponsorship to the Python Software Foundation.

Continued investment in quantum education Trying to explain quantum computing without resorting to incorrect analogies has always been a goal for our team. As a result, we have continuously invested in education, starting with opening access to quantum computers, and continuing to create tools that enable anyone to program them. Notably, we created the first interactive open source textbook in the field.

As developers program quantum computers, what they are really doing is building and running quantum circuits. To support your learning about quantum circuits:

Read the Qiskit textbook chapter where we define quantum circuits as we understand them today. Dive in to explore quantum computing principles and learn how to implement quantum algorithms on your own. Watch our newly launched livelectures called Circuit Sessions, or get started programming a quantum computer by watching Coding with Qiskit. Subscribe to the Qiskit YouTube channel to watch these two series and more. The future of quantum is in open source software and access to real quantum hardwarelets keep building together.

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Announcing the IBM Quantum Challenge - Quantaneo, the Quantum Computing Source

Global Quantum Computing Market 2020 Industry Trends, Growth Opportunities, Industry Revenue, and Business Analysis by Forecast 2026 Cole Reports -…

In its currently appended report by Magnifier Research with the title Global Quantum Computing Market Size, Status and Forecast 2020-2026 has incorporated statistics and data associated with the market. The report provides an inclusive analysis of the market structure which involves distinctive perceptions about the market for a projected time period from 2020 to 2026. The report analyzes the performance of the existing scenario of the global Quantum Computing market. The report provides helpful information regarding the current trends in the market. It mainly showcases market size, market share, market trends, development rate, and other important market elements.

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The report explores the recent significant developments by the leading vendors and innovation profiles in the global Quantum Computing market including are: D-Wave Systems, 1QB Information Technologies, QxBranch LLC, QC Ware Corp, Research at Google-Google,

As part of the geographic evaluation of the international global Quantum Computing industry, this research digs deep into the boom of key regions and countries, consisting of but no longer confined to North America (United States, Canada, Mexico), Asia-Pacific (China, Japan, South Korea, India, Australia, Indonesia, Thailand, Malaysia, Philippines, Vietnam), Europe (Germany, France, UK, Italy, Russia, Rest of Europe), Central & South America (Brazil, Rest of South America), Middle East & Africa (GCC Countries, Turkey, Egypt, South Africa, Rest of Middle East & Africa)

On the basis of product type, this report displays the shipments, revenue (Million USD), price, and market share and growth rate of each type:

On the basis on the end users/applications, this report focuses on the status and outlook for major applications/end users, shipments, revenue (Million USD), price, and market share and growth rate for each application: Defense, Banking & Finance, Energy & Power, Chemicals, Healthcare & Pharmaceuticals,

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Other terms covered in the report include historic, current and future market analysis, industry players, cost structure, and project feasibility analysis of key manufacturers for 2020 to 2026 forecast period. The current global Quantum Computing market scenario, revenue statistics of the market and sales rate that each firm is expected to attain during the forecast period are further provided in the report. The revenue share hold by different geographies at present condition is given in the report. Readers of the report are expected to receive useful guidelines on how to make your companys presence known in the market as well as increase its share in the coming years. Moreover, information regarding the analysis of new projects undertaken as well as the conclusions has been given in the report.

Customization of the Report:This report can be customized to meet the clients requirements. Please connect with our sales team ([emailprotected]), who will ensure that you get a report that suits your needs. You can also get in touch with our executives on +1-201-465-4211 to share your research requirements.

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Magnifier Research is a leading market intelligence company that sells reports of top publishers in the technology industry. Our extensive research reports cover detailed market assessments that include major technological improvements in the industry. Magnifier Research also specializes in analyzing hi-tech systems and current processing systems in its expertise. We have a team of experts that compile precise research reports and actively advise top companies to improve their existing processes. Our experts have extensive experience in the topics that they cover. Magnifier Research provides you the full spectrum of services related to market research, and corroborate with the clients to increase the revenue stream, and address process gaps.

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Quantum Computing Market Growth Opportunities, Challenges, Key Companies, Drivers and Forecast to 2026 Cole Reports – Cole of Duty

1qb Information Technologies

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This section of the report lists various major manufacturers in the market. The competitive analysis helps the reader understand the strategies and collaborations that players focus on in order to survive in the market. The reader can identify the players fingerprints by knowing the companys total sales, the companys total price, and its production by company over the 2020-2026 forecast period.

Global Quantum Computing Market: Regional Analysis

The report provides a thorough assessment of the growth and other aspects of the Quantum Computing market in key regions, including the United States, Canada, Italy, Russia, China, Japan, Germany, and the United Kingdom United Kingdom, South Korea, France, Taiwan, Southeast Asia, Mexico, India and Brazil, etc. The main regions covered by the report are North America, Europe, the Asia-Pacific region and Latin America.

The Quantum Computing market report was prepared after various factors determining regional growth, such as the economic, environmental, technological, social and political status of the region concerned, were observed and examined. The analysts examined sales, production, and manufacturer data for each region. This section analyzes sales and volume by region for the forecast period from 2020 to 2026. These analyzes help the reader understand the potential value of investments in a particular country / region.

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COMMENTARY: Biden should campaign on theme: Is this really the best we can do? – Utica Observer Dispatch

In speeches during the 1960 presidential campaign, John Kennedy addressed Americans' anxiety about national lassitude at the end of eight years under Dwight Eisenhower by mildly saying: "I believe we can do better."

Joe Biden, responding to national embarrassment about the least presidential president, can campaign on a modest theme: "Is 'this' really the best we can do?"

This question answers itself, particularly concerning foreign policy. Fortunately for Biden, events and his opponent are making this central to the 2020 election.

It is axiomatic that Americans' preference regarding foreign policy is to have as little of it as possible. Hence most of this cycle's Democratic presidential aspirants avoided reminding people that the world is a dangerous place. However, in the Feb. 25 debate in Charleston, South Carolina, Biden called China's President Xi Jinping "a thug": "This is a guy who doesn't have a democratic-with-a-small-'d' bone in his body."

Economist John Maynard Keynes supposedly said, "When the facts change, I change my mind." Biden, citing new facts, including aggression against Hong Kong's freedom and "a million Uighurs" in "concentration camps," has jettisoned his 2016 talk of his "enhanced cooperation" with Xi.

In 34 of Biden's 36 Senate years, he was on the Foreign Relations Committee, which he chaired for four years. Donald Trump's foreign policy judgments have ranged from the contemptible (siding with Vladimir Putin at Helsinki in 2018 against U.S. intelligence officials regarding Russian interference in the 2016 election) to the preposterous ("There is no longer a Nuclear Threat from North Korea") to the weird (he and North Korea's Kim Jong Un "fell in love" after exchanging "beautiful letters").

Trump now wants to make relations with China central to this campaign. His rhetorical skills -- probably honed where they evidently peaked, on grammar school playgrounds are emulated by his campaign in references to "Beijing Biden." Biden can, however, turn China to his advantage by showing Trump what a policy of national strength would look like.

Biden served in the Senate for a decade with Sen. Henry Jackson, D-Wash., a liberal Cold Warrior who helped to make the Soviet Union's human-rights abuses costly to the regime. Today, Biden should speak forcefully against China's arrests of Martin Lee, 81, Jimmy Lai, 71, Margaret Ng, 72, and other leaders of Hong Kong's democracy movement.

Biden can practice what he preaches about bipartisanship by associating himself with Arkansas Republican Sen. Tom Cotton's measured but insistent support for the investigation of the possible role of a Wuhan research laboratory in the coronavirus outbreak. And with former U.S. ambassador to the United Nations Nikki Haley's call to require U.S. universities to disclose China's funding of their professors and research.

Cotton questions the visas for Chinese to pursue postgraduate studies here in advanced science and technology fields: If Chinese students want to study "Shakespeare and the Federalist Papers, that's what they need to learn from America. They don't need to learn quantum computing and artificial intelligence from America."

In February, a senior adviser for the World Health Organization's director-general praised China's "bold approach" that "changed the course" of the epidemic. Indeed China did: Its first approach was to deny that there is human-to-human transmission.

Biden should say that continued U.S. participation in this organization will be contingent upon its granting Taiwan membership. Biden should also promise to discuss Taiwan's exemplary response to COVID-19 with Tsai Ing-wen "in the Oval Office. She would be the first Taiwanese president welcomed in the United States since the 1979 "normalization" of relations with China.

By taking such steps, Biden can reconnect his party with its luminous post-1945 achievement. In that golden moment in the history of this nation's engagement with the world, the talents of Dean Acheson, George Marshall, George Kennan, Averell Harriman, Robert Lovett, Charles Bohlen, John McCloy and others created the structures of free trade and collective military security that produced the related phenomena of global enrichment and Soviet collapse.

The winners of the past seven presidential elections (1992-2016) have averaged 330 electoral votes. If today's state-by-state polls are correct, and if the election were held today, Biden would win 333 electoral votes: 227 from Hillary Clinton's states plus those from Wisconsin, Michigan, Pennsylvania, Florida, Arizona and North Carolina.

More than any particular policy outcome, Americans want a sense that their nation can regain the spring in its step, and can adopt a robust realism regarding the Leninist party-state that is its principal adversary. The first step toward a jauntier, safer America is to make the election a referendum on the right question: "Is 'this' really the best we can do?"

George Will is a columnist for the Washington Post. Email him at georgewill@washpost.com

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COMMENTARY: Biden should campaign on theme: Is this really the best we can do? - Utica Observer Dispatch

Global Quantum Computing Market : Industry Analysis and Forecast (2020-2027) – MR Invasion

Global Quantum Computing Marketwas valued US$ 198.31 Mn in 2019 and is expected to reach US$ 890.5 Mn by 2027, at CAGR of 28.44 % during forecast.

The report study has analyzed revenue impact of covid-19 pandemic on the sales revenue of market leaders, market followers and disrupters in the report and same is reflected in our analysis.

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Quantum computing market growth is being driven by factors like increasing incidences of cybercrime, early adoption of quantum computing technology in automotive and defense industry, and growing investments by government entities in quantum computing market. On the other hand, presence of substitute technology and reluctance to accept new technology are factors limiting the growth of quantum computing market.

Quantum computing market in the energy & power industry is projected to witness a CAGR of 40% from 2017 to 2023. This growth is primarily attributed to the beneficial opportunities existing in the nuclear and renewable sector. Applications like energy exploration, seismic survey optimization, and reservoir optimization are estimated to lead this industry in quantum computing market.

North America was holding the largest market share of quantum computing market in 2016. North America is a key market as it is the home ground for some of the major corporations like D-Wave Systems Inc., 1QB Information Technologies, Inc. The increased research and development (R&D) activities in the sector of quantum computing are directed in this region as well as the heavy investments by government activities and technologically advanced players International Business Machines Corporation, Microsoft Corporation, Google Inc., and Intel Corporation are factors driving the growth of quantum computing market in North America. The R&D at industry levels is extending the application areas of the quantum computing market in various industries like energy & power, defense, and chemicals, especially in US.

Owing to the economic interest and decline of Moores law of computational scaling, eighteen of the worlds biggest corporations and dozens of government organizations are working on quantum processor technologies and quantum software or associating with the quantum industry startups like D-Wave. Their determination reflects a wider transition, taking place at start-ups and academic research labs like move from pure science towards engineering.

Quantum computing market report evaluates the technology, companies/associations, R&D efforts, and potential solutions assisted by quantum computing. It also estimates the impact of quantum computing on other major technologies and solution areas with AI, chipsets, edge computing, blockchain, IoT, big data analytics, and smart cities. This report offers global and regional forecasts as well the viewpoint for quantum computing impact on hardware, software, applications, and services

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The objective of the report is to present a comprehensive assessment of the market and contains thoughtful insights, facts, historical data, industry-validated market data and projections with a suitable set of assumptions and methodology. The report also helps in understanding Quantum Computing market dynamics, structure by identifying and analyzing the market segments and project the global market size. Further, report also focuses on competitive analysis of key players by product, price, financial position, product portfolio, growth strategies, and regional presence. The report also provides PEST analysis, PORTERs analysis, SWOT analysis to address questions of shareholders to prioritizing the efforts and investment in near future to emerging segment in Quantum Computing market.Scope of Global Quantum Computing Market:

Global Quantum Computing Market, by Technology:

Superconducting loops technology Trapped ion technology Topological qubits technologyGlobal Quantum Computing Market, by Application:

Simulation Optimization SamplingGlobal Quantum Computing Market, by Component:

Hardware Software ServicesGlobal Quantum Computing Market, by Industry:

Defense Banking & Finance Energy & Power Chemicals Healthcare & PharmaceuticalsGlobal Quantum Computing Market, by Region:

North America Asia Pacific Europe Latin America Middle East & AfricaKey Players Operating in Market Include:

D-Wave Systems Inc 1QB Information Technologies Inc. QxBranch LLC QC Ware Corp. and Research at Google-Google Inc. International Business Machines Corporation Lockheed Martin Corporation Intel Corporation Anyon Systems Inc. Cambridge Quantum Computing Limited Rigetti Computing Magiq Technologies Inc. Station Q Microsoft Corporation IonQ Quantum Computing Software Start-ups Qbit Alibaba Ariste-QB.net Atos Q-Ctrl Qu and Co Quantum Benchmark SAP Turing Zapata

MAJOR TOC OF THE REPORT

Chapter One: Quantum Computing Market Overview

Chapter Two: Manufacturers Profiles

Chapter Three: Global Quantum Computing Market Competition, by Players

Chapter Four: Global Quantum Computing Market Size by Regions

Chapter Five: North America Quantum Computing Revenue by Countries

Chapter Six: Europe Quantum Computing Revenue by Countries

Chapter Seven: Asia-Pacific Quantum Computing Revenue by Countries

Chapter Eight: South America Quantum Computing Revenue by Countries

Chapter Nine: Middle East and Africa Revenue Quantum Computing by Countries

Chapter Ten: Global Quantum Computing Market Segment by Type

Chapter Eleven: Global Quantum Computing Market Segment by Application

Chapter Twelve: Global Quantum Computing Market Size Forecast (2019-2026)

Browse Full Report with Facts and Figures of Quantum Computing Market Report at:https://www.maximizemarketresearch.com/market-report/global-quantum-computing-market/27533/

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Global Quantum Computing Market : Industry Analysis and Forecast (2020-2027) - MR Invasion

NGA Knows Its Challenges, Now It Needs the Tech to Address Them – Nextgov

The National Geospatial-Intelligence Agency released its second annual Tech Focus Areas, highlighting top problem areas the agency wants to address using technology in the coming year.

While NGA published a similar list in 2019, this years authors note the world has changed significantly.

The year 2020 will represent a historic inflection point for our agency, our community, and our nation. In addition to the challenges we currently face from the COVID-19 pandemic, great power competition has reemerged as another challenge to U.S. prosperity and security, the document states.

The nature of geospatial-intelligence, or GEOINT, has changed significantly as well. In the past, the U.S. government was the undisputed leader in global surveillance.

Today, with commercial GEOINT available worldwide, we face a much more level playing field, the document states.

Several near-peer adversaries are investing significantly in new technologies to close the gap with U.S. and allied capabilities, NGA Chief Technology Officer Mark Munsell wrote in an introductory note. To stay ahead of these adversaries, we must bring together our world-class experts at NGA, industry partners with exquisite domain expertise and technical capabilities, and companies who have never worked with government before but whose products could help advance NGAs mission.

As such, Munsell said the document was designed to focus on areas of need, rather than specific technologies. While the document does not address specific technological solutions, it is explicitin broad termsabout the kinds of technologies NGA wants to explore.

In order to maintain leadership in this realm, NGA plans to foster partnerships with other agencies, industry and academia.

The tech focus areas arent shelfwarewe are identifying opportunities to leverage non-traditional acquisition capabilities to address the needs outlined in this document, Christy Monaco, NGA chief ventures officer, said in a release Wednesday.

The extensive list of needs includes things like analyzing immense data sets to provide useful models; managing and integrating data from diverse sources; improving the software development pipeline; taking advantage of advances in artificial intelligence and quantum computing; and preparing the agency for the future of work, including managing a distributed workforce.

The document condenses all this into five focus areas, each with several subsections explaining the agencys needs. From the document:

Advanced Analytics and Modeling

Data Management

Modern Software Engineering

Artificial Intelligence

Future of Work

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NGA Knows Its Challenges, Now It Needs the Tech to Address Them - Nextgov

Global Machine Learning As A Service (Mlaas) Market : Industry Analysis And Forecast (2020-2027) – MR Invasion

Global Machine Learning as a Service (MLaaS) Marketwas valued about US$ XX Bn in 2019 and is expected to grow at a CAGR of 41.7% over the forecast period, to reach US$ 11.3 Bn in 2027.

The report study has analyzed revenue impact of covid-19 pandemic on the sales revenue of market leaders, market followers and disrupters in the report and same is reflected in our analysis.

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Market Definition:

Machine learning as a service (MLaaS) is an array of services that offer ML tools as part of cloud computing services. MLaaS helps clients profit from machine learning without the cognate cost, time and risk of establishing an in-house internal machine learning team.The report study has analyzed revenue impact of covid-19 pandemic on the sales revenue of market leaders, market followers and disrupters in the report and same is reflected in our analysis.

Machine Learning Service Providers:

Global Machine Learning as a Service (MLaaS) Market

Market Dynamics:

The scope of the report includes a detailed study of global and regional markets for Global Machine Learning as a Service (MLaaS) Market with the analysis given with variations in the growth of the industry in each regions. Large and SMEs are focusing on customer experience management to keep a complete and robust relationship with their customers by using customer data. So, ML needs to be integrated into enterprise applications to control and make optimal use of this data. Retail enterprises are shifting their focus to customer buying patterns with the rising number of e-commerce websites and the digital revolution in the retail industry. This drives the need to track and manage the inventory movement of items, which can be done using MLaaS. The use of MLaaS by retail enterprises for inventory optimization and behavioral tracking is expected to have a positive impact on global market growth.Apart from this, the growing trend of digitization is driving the growth of the MLaaS market globally. Growth in adoption of cloud-based platforms is expected to positively impact the growth of the MLaaS market. However, a lack of qualified and skilled persons is believed to be the one of the challenges before the growth of the MLaaS market. Furthermore, increasing concern toward data privacy is anticipated to restrain the development of the global market.

Market Segmentation:

The report will provide an accurate prediction of the contribution of the various segments to the growth of the Machine Learning as a Service (MLaaS) Market size. Based on organization size, SMEs segment is expected to account for the largest XX% market share by 2027. SMEs businesses are also projected to adopt machine learning service. With the help of predictive analytics ML, algorithms not only give real-time data but also predict the future. Machine learning solutions are used by SME businesses for fine-tuning their supply chain by predicting the demand for a product and by suggesting the timing and quantity of supplies vital for satisfying the customers expectations.

Regional Analysis:

The report offers a brief analysis of the major regions in the MLaaS market, namely, Asia-Pacific, Europe, North America, South America, and the Middle East & Africa.North America play an important role in MLaaS market, with a market size of US$ XX Mn in 2019 and will be US$ XX Mn in 2027, with a CAGR of XX% followed by Europe. Most of the machine learning as service market companies are based in the U.S and are contributing significantly in the growth of the market. The Asia-Pacific has been growing with the highest growth rate because of rising investment, favorable government policies and growing awareness. In 2017, Google launched the Google Neural Machine Translation for 9 Indian languages which use ML and artificial neural network to upsurges the fluency as well as accuracy in their Google Translate.

Recent Development:

The MMR research study includes the profiles of leading companies operating in the Global Machine Learning as a Service (MLaas) Market. Companies in the global market are more focused on enhancing their product and service helps through various strategic approaches. The ML providers are competing by launching new product categories, with advanced subscription-based platforms. The companies have adopted the strategy of version up gradations, mergers and acquisitions, agreements, partnerships, and strategic collaborations with regional and global players to achieve high growth in the MLaaS market.

Such as, in April 2019, Microsoft developed a platform that uses machine teaching to help deep strengthening learning algorithms tackle real-world problems. Microsoft scientists and product inventors have pioneered a complementary approach called ML. This relies on people know how to break a problem into easier tasks and give ML models important clues about how to find a solution earlier.

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The objective of the report is to present a comprehensive analysis of the Global Machine Learning as a Service (MLaaS) Market including all the stakeholders of the industry. The past and current status of the industry with forecasted market size and trends are presented in the report with the analysis of complicated data in simple language. The report covers all the aspects of the industry with a dedicated study of key players that includes market leaders, followers and new entrants by region. PORTER, SVOR, PESTEL analysis with the potential impact of micro-economic factors by region on the market has been presented in the report. External as well as internal factors that are supposed to affect the business positively or negatively have been analyzed, which will give a clear futuristic view of the industry to the decision-makers.

The report also helps in understanding Global Machine Learning as a Service (MLaaS) Market dynamics, structure by analyzing the market segments and projects the Global Machine Learning as a Service (MLaaS) Market size. Clear representation of competitive analysis of key players by Application, price, financial position, Product portfolio, growth strategies, and regional presence in the Global Machine Learning as a Service (MLaaS) Market make the report investors guide.Scope of the Global Machine Learning as a Service (MLaaS) Market

Global Machine Learning as a Service (MLaaS) Market, By Component

Software ServicesGlobal Machine Learning as a Service (MLaaS) Market, By Organization Size

Large Enterprises SMEsGlobal Machine Learning as a Service (MLaaS) Market, By End-Use Industry

Aerospace & Defense IT & Telecom Energy & Utilities Public sector Manufacturing BFSI Healthcare Retail OthersGlobal Machine Learning as a Service (MLaaS) Market, By Application

Marketing & Advertising Fraud Detection & Risk Management Predictive analytics Augmented & Virtual reality Natural Language processing Computer vision Security & surveillance OthersGlobal Machine Learning as a Service (MLaaS) Market, By Region

Asia Pacific North America Europe Latin America Middle East AfricaKey players operating in Global Machine Learning as a Service (MLaaS) Market

Ersatz Labs, Inc. BigML Yottamine Analytics Hewlett Packard Amazon Web Services IBM Microsoft Sift Science, Inc. Google AT&T Fuzzy.ai SAS Institute Inc. FICO Predictron Labs Ltd.

MAJOR TOC OF THE REPORT

Chapter One: Machine Learning as a Service Market Overview

Chapter Two: Manufacturers Profiles

Chapter Three: Global Machine Learning as a Service Market Competition, by Players

Chapter Four: Global Machine Learning as a Service Market Size by Regions

Chapter Five: North America Machine Learning as a Service Revenue by Countries

Chapter Six: Europe Machine Learning as a Service Revenue by Countries

Chapter Seven: Asia-Pacific Machine Learning as a Service Revenue by Countries

Chapter Eight: South America Machine Learning as a Service Revenue by Countries

Chapter Nine: Middle East and Africa Revenue Machine Learning as a Service by Countries

Chapter Ten: Global Machine Learning as a Service Market Segment by Type

Chapter Eleven: Global Machine Learning as a Service Market Segment by Application

Chapter Twelve: Global Machine Learning as a Service Market Size Forecast (2019-2026)

Browse Full Report with Facts and Figures of Machine Learning as a Service Market Report at:https://www.maximizemarketresearch.com/market-report/global-machine-learning-as-a-service-mlaas-market/55511/

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Global Machine Learning As A Service (Mlaas) Market : Industry Analysis And Forecast (2020-2027) - MR Invasion

Could Machine Learning Replace the Entire Weather Forecast System? – HPCwire

Just a few months ago, a series of major new weather and climate supercomputing investments were announced, including a 1.2 billion order for the worlds most powerful weather and climate supercomputer and a tripling of the U.S. operational supercomputing capacity for weather forecasting. Weather and climate modeling are among the most power-hungry use cases for supercomputers, and research and forecasting agencies often struggle to keep up with the computing needs of models that are, in many cases, simulating the atmosphere of the entire planet as granularly and as regularly as possible.

What if that all changed?

In a virtual keynote for the HPC-AI Advisory Councils 2020 Stanford Conference, Peter Dueben outlined how machine learning might (or might not) begin to augment and even, eventually, compete with heavy-duty, supercomputer-powered climate models. Dueben is the coordinator for machine learning and AI activities at the European Centre for Medium-Range Weather Forecasts (ECMWF), a UK-based intergovernmental organization that houses two supercomputers and provides 24/7 operational weather services at several timescales. ECMWF is also the home of the Integrated Forecast System (IFS), which Dueben says is probably one of the best forecast models in the world.

Why machine learning at all?

The Earth, Dueben explained, is big. So big, in fact, that apart from being laborious, developing a representational model of the Earths weather and climate systems brick-by-brick isnt achieving the accuracy that you might imagine. Despite the computing firepower behind weather forecasting, most models remain at a 10 kilometer resolution that doesnt represent clouds, and the chaotic atmospheric dynamics and occasionally opaque interactions further complicate model outputs.

However, on the other side, we have a huge number of observations, Dueben said. Just to give you an impression, ECMWF is getting hundreds of millions of observations onto the site every day. Some observations come from satellites, planes, ships, ground measurements, balloons This data collected over the last several decades constituted hundreds of petabytes if simulations and climate modeling results were included.

If you combine those two points, we have a very complex nonlinear system and we also have a lot of data, he said. Theres obviously lots of potential applications for machine learning in weather modeling.

Potential applications of machine learning

Machine learning applications are really spread all over the entire workflow of weather prediction, Dueben said, breaking that workflow down into observations, data assimilation, numerical weather forecasting, and post-processing and dissemination. Across those areas, he explained, machine learning could be used for anything from weather data monitoring to learning the underlying equations of atmospheric motions.

By way of example, Dueben highlighted a handful of current, real-world applications. In one case, researchers had applied machine learning to detecting wildfires caused by lightning. Using observations for 15 variables (such as temperature, soil moisture and vegetation cover), the researchers constructed a machine learning-based decision tree to assess whether or not satellite observations included wildfires. The team achieved an accuracy of 77 percent which, Deuben said, doesnt sound too great in principle, but was actually quite good.

Elsewhere, another team explored the use of machine learning to correct persistent biases in forecast model results. Dueben explained that researchers were examining the use of a weak constraint machine learning algorithm (in this case, 4D-Var), which is a kind of algorithm that would be able to learn this kind of forecast error and correct it in the data assimilation process.

We learn, basically, the bias, he said, and then once we have learned the bias, we can correct the bias of the forecast model by just adding forcing terms to the system. Once 4D-Var was implemented on a sample of forecast model results, the biases were ameliorated. Though Dueben cautioned that the process is still fairly simplistic, a new collaboration with Nvidia is looking into more sophisticated ways of correcting those forecast errors with machine learning.

Dueben also outlined applications in post-processing. Much of modern weather forecasting focuses on ensemble methods, where a model is run many times to obtain a spread of possible scenarios and as a result, probabilities of various outcomes. We investigate whether we can correct the ensemble spread calculated from a small number of ensemble members via deep learning, Dueben said. Once again, machine learning when applied to a ten-member ensemble looking at temperatures in Europe improved the results, reducing error in temperature spreads.

Can machine learning replace core functionality or even the entire forecast system?

One of the things that were looking into is the emulation of different permutation schemes, Dueben said. Chief among those, at least initially, have been the radiation component of forecast models, which account for the fluxes of solar radiation between the ground, the clouds and the upper atmosphere. As a trial run, Dueben and his colleagues are using extensive radiation output data from a forecast model to train a neural network. First of all, its very, very light, Dueben said. Second of all, its also going to be much more portable. Once we represent radiation with a deep neural network, you can basically port it to whatever hardware you want.

Showing a pair of output images, one from the machine learning model and one from the forecast model, Dueben pointed out that it was hard to notice significant differences and even refused to tell the audience which was which. Furthermore, he said, the model had achieved around a tenfold speedup. (Im quite confident that it will actually be much better than a factor of ten, Dueben said.)

Dueben and his colleagues have also scaled their tests up to more ambitious realms. They pulled hourly data on geopotential height (Z500) which is related to air pressure and trained a deep learning model to predict future changes in Z500 across the globe using only that historical data. For this, no physical understanding is really required, Dueben said, and it turns out that its actually working quite well.

Still, Dueben forced himself to face the crucial question.

Is this the future? he asked. I have to say its probably not.

There were several reasons for this. First, Dueben said, the simulations were unstable, eventually blowing up if they were stretched too far. Second of all, he said, its also unknown how to increase complexity at this stage. We only have one field here. Finally, he explained, there were only forty years of sufficiently detailed data with which to work.

Still, it wasnt all pessimism. Its kind of unlikely that its going to fly and basically feed operational forecasting at one point, he said. However, having said this, there are now a number of papers coming out where people are looking into this in a much, much more complicated way than we have done with really sophisticated convolutional networks and they get, actually, quite good results. So who knows!

The path forward

The main challenge for machine learning in the community that were facing at the moment, Dueben said, is basically that we need to prove now that machine learning solutions can really be better than conventional tools and we need to do this in the next couple of years.

There are, of course, many roadblocks to that goal. Forecasting models are extraordinarily complicated; iterations on deep learning models require significant HPC resources to test and validate; and metrics of comparison among models are unclear. Dueben also outlined a series of major unknowns in machine learning for weather forecasting: could our explicit knowledge of atmospheric mechanisms be used to improve a machine learning forecast? Could researchers guarantee reproducibility? Could the tools be scaled effectively to HPC? The list went on.

Many scientists are working on these dilemmas as we speak, Dueben said, and Im sure we will have an enormous amount of progress in the next couple of years. Outlining a path forward, Dueben emphasized a mixture of a top-down and a bottom-up approach to link machine learning with weather and climate models. Per his diagram, this would combine neutral networks based on human knowledge of earth systems with reliable benchmarks, scalability and better uncertainty quantification.

As far as where he sees machine learning for weather prediction in ten years?

It could be that machine learning will have no long-term effect whatsoever that its just a wave going through, Dueben mused. But on the other hand, it could well be that machine learning tools will actually replace almost all conventional models that were working with.

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Could Machine Learning Replace the Entire Weather Forecast System? - HPCwire

Is Machine Learning Model Management The Next Big Thing In 2020? – Analytics India Magazine

ML and its services are only going to extend their influence and push the boundaries to new realms of the technology revolution. However, deploying ML comes with great responsibility. Though efforts are being made to shed its black box reputation, it is crucial to establish trust in both in-house teams and stakeholders for a fairer deployment. Companies have started to take machine learning model management more seriously now. Recently, a machine learning company Comet.ml, based out of Seattle and founded in 2017, announced that they are making a $4.5 million investment to bring state-of-the-art meta-learning capabilities to the market.

The tools developed by Comet.ml enable data scientists to track, compare, monitor, and optimise model development. Their announcement of an additional $4.5 million investment from existing investors Trilogy Equity Partners and Two Sigma Ventures is aimed at boosting their plans to domesticate the use of machine learning model management techniques to more customers.

Since their product launch in 2018, Comet.ml has partnered with top companies like Google, General Electric, Boeing and Uber. This elite list of customers use comet.al services, which have enterprise-level toolkits, and are used to train models across multiple industries spanning autonomous vehicles, financial services, technology, bioinformatics, satellite imagery, fundamental physics research, and more.

Talking about this new announcement, one of the investors, Yuval Neeman of Trilogy Equity Partners, reminded that the professionals from the best companies in the world choose Comet and that the company is well-positioned to become the de-facto Machine Learning development platform.

This platform, says Neeman, allows customers to build ML models that bring significant business value.

According to a report presented by researchers at Google, there are several ML-specific risk factors to account for in system design, such as:

Debugging all these issues require round the clock monitoring of the models pipeline. For a company that implements ML solutions, it is challenging to manage in-house model mishaps.

If we take the example of Comet again, its platform provides a central place for the team to track their ML experiments and models, so that they can compare and share experiments, debug and take decisive actions on underperforming models with great ease.

Predictive early stopping is a meta-learning functionality not seen in any other experimentation platforms, and this can be achieved only by building on top of millions of public models. And this is where Comets enterprise products come in handy. The freedom of experimentation that these meta learning-based platforms offer is what any organisation would look up to. Almost all ML-based companies would love to have such tools in their arsenal.

Talking about saving the resources, Comet.ml in their press release, had stated that their platform led to the improvement of model training time by 30% irrespective of the underlying infrastructure, and stopped underperforming models automatically, which reduces cost and carbon footprint by 30%.

Irrespective of the underlying infrastructure, it stops underperforming models automatically, which reduces cost and carbon footprint by 30%.

The enterprise offering also includes Comets flagship visualisation engine, which allows users to visualise, explain, and debug model performance and predictions, and a state-of-the-art parameter optimisation engine.

When building any machine learning pipeline, data preparation requires operations like scraping, sampling, joining, and plenty of other approaches. These operations usually accumulate haphazardly and result in what the software engineers would like to call a pipeline jungle.

Now, add in the challenge of forgotten experimental code in the code archives. Things only get worse. The presence of such stale code can malfunction, and an algorithm that runs this malfunctioning code can crash stock markets and self-driving cars. The risks are just too high.

So far, we have seen the use of ML for data-driven solutions. Now the market is ripe for solutions that help those who have already deployed machine learning. It is only a matter of time before we see more companies setting up their meta-learning shops or partner with third-party vendors.

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Is Machine Learning Model Management The Next Big Thing In 2020? - Analytics India Magazine

Harnessing the power of GaN and machine learning – News – Compound Semiconductor

Military installations, especially on ships and aircraft, require robust power electronics systems to operate radar and other equipment, but there is limited space onboard. Researchers from the University of Houston will use a $2.5 million grant from the US Department of Defense to develop compact electronic power systems to address the issue.

Harish Krishnamoorthy, assistant professor of electrical and computer engineering and principal investigator for the project, said he will focus on developing power converters using GaN (GaN) devices, capable of quickly storing and discharging energy to operate the radar systems.

He is working with co-PI Kaushik Rajashekara, professor of electrical and computer engineering, and Tagore Technology, a semiconductor company based in Arlington Heights, Ill. The work has potential commercial applications, in addition to military use, he said.

Currently, radar systems require large capacitors, which store energy and provide bursts of power to operate the systems. The electrolytic capacitors also have relatively short lifespans, Krishnamoorthy said.

GaN devices can be turned on and off far more quickly - over ten times as quickly as silicon devices. The resulting higher operating frequency allows passive components in the circuit - including capacitors and inductors - to be designed at much smaller dimensions.

But there are still drawbacks to GaN devices. Noise - electromagnetic interference, or EMI - can affect the precision of radar systems, since the devices work at such high speeds. Part of Krishnamoorthy's project involves designing a system where converters can contain the noise, allowing the radar system to operate unimpeded.

He also will use machine learning to predict the lifespan of GaN devices, as well as of circuits employing these devices. The use of GaN technology in power applications is relatively new, and assessing how long they will continue to operate in a circuit remains a challenge.

"We don't know how long these GaN devices will last in practical applications, because they've only been used for a few years," Krishnamoorthy said. "That's a concern for industry."

The health and well-being of AngelTech speakers, partners, employees and the overall community is our top priority. Due to the growing concern around the coronavirus (COVID-19), and in alignment with the best practices laid out by the CDC, WHO and other relevant entities, AngelTech decided to postpone the live Brussels event to 16th - 18th November 2020.

In the interim, we believe it is still important to connect the community and we want to do this via an online summit, taking place live on Tuesday May 19th at 12:00 GMT and content available for 12 months on demand. This will not replace the live event (we believe live face to face interaction, learning and networking can never be fully replaced by a virtual summit), it will supplement the event, add value for key players and bring the community together digitally.

The event will involve 4 breakout sessions for CS International, PIC International, Sensors International and PIC Pilot Lines.

Key elements of the online summit:

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Harnessing the power of GaN and machine learning - News - Compound Semiconductor

Yoshua Bengio: Attention is a core ingredient of conscious AI – VentureBeat

During the International Conference on Learning Representations (ICLR) 2020 this week, which as a result of the pandemic took place virtually, Turing Award winner and director of the Montreal Institute for Learning Algorithms Yoshua Bengio provided a glimpse into the future of AI and machine learning techniques. He spoke in February at the AAAI Conference on Artificial Intelligence 2020 in New York alongside fellow Turing Award recipients Geoffrey Hinton and Yann LeCun. But in a lecture published Monday, Bengio expounded upon some of his earlier themes.

One of those was attention in this context, the mechanism by which a person (or algorithm) focuses on a single element or a few elements at a time. Its central both to machine learning model architectures like Googles Transformer and to the bottleneck neuroscientific theory of consciousness, which suggests that people have limited attention resources, so information is distilled down in the brain to only its salient bits. Models with attention have already achieved state-of-the-art results in domains like natural language processing, and they could form the foundation of enterprise AI that assists employees in a range of cognitively demanding tasks.

Bengio described the cognitive systems proposed by Israeli-American psychologist and economist Daniel Kahneman in his seminal book Thinking, Fast and Slow. The first type is unconscious its intuitive and fast, non-linguistic and habitual, and it deals only with implicit types of knowledge. The second is conscious its linguistic and algorithmic, and it incorporates reasoning and planning, as well as explicit forms of knowledge. An interesting property of the conscious system is that it allows the manipulation of semantic concepts that can be recombined in novel situations, which Bengio noted is a desirable property in AI and machine learning algorithms.

Current machine learning approaches have yet to move beyond the unconscious to the fully conscious, but Bengio believes this transition is well within the realm of possibility. He pointed out that neuroscience research has revealed that the semantic variables involved in conscious thought are often causal they involve things like intentions or controllable objects. Its also now understood that a mapping between semantic variables and thoughts exists like the relationship between words and sentences, for example and that concepts can be recombined to form new and unfamiliar concepts.

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Attention is one of the core ingredients in this process, Bengio explained.

Building on this, in a recent paper he and colleagues proposed recurrent independent mechanisms (RIMs), a new model architecture in which multiple groups of cells operate independently, communicating only sparingly through attention. They showed that this leads to specialization among the RIMs, which in turn allows for improved generalization on tasks where some factors of variation differ between training and evaluation.

This allows an agent to adapt faster to changes in a distribution or inference in order to discover reasons why the change happened, said Bengio.

He outlined a few of the outstanding challenges on the road to conscious systems, including identifying ways to teach models to meta-learn (or understand causal relations embodied in data) and tightening the integration between machine learning and reinforcement learning. But hes confident that the interplay between biological and AI research will eventually unlock the key to machines that can reason like humans and even express emotions.

Consciousness has been studied in neuroscience with a lot of progress in the last couple of decades. I think its time for machine learning to consider these advances and incorporate them into machine learning models.

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Yoshua Bengio: Attention is a core ingredient of conscious AI - VentureBeat

This AI tool uses machine learning to detect whether people are social distancing properly – Mashable SE Asia

Perhaps the most important step we can all take to mitigate the spread of the coronavirus, also known as COVID-19, is to actively practice social distancing.

Why? Because the further away you are from another person, the less likely you'll contract or transmit COVID-19.

But when we go about our daily routines, especially when out on a grocery run or heading to the hospital, social distancing can be a challenging task to uphold.

And some of us just have God awful spatial awareness in general.

But how do we monitor and enforce social distancing when looking at a mass population? We resort to the wonders of artificial intelligence (AI), of course.

In a recent blog post, the company demonstrated a nifty social distancing detector that shows a feed of people walking along a street in the Oxford Town Center of the United Kingdom.

The tool encompasses every individual in the feed with a rectangle. When they're properly observing social distancing, that rectangle is green. But when they get too close to another person (less than 6 feet away), the rectangle turns red, accompanied by a line 'linking' the two people that are too close to one another.

On the right-hand side of the tool there's a 'Bird's-Eye View' that allows for monitoring on a bigger scale. Every person is represented by a dot. Working the same way as the rectangles, the dots are green when social distancing is properly adhered to. They turn red when people get too close.

More specifically, work settings like factory floors where physical space is abundant, thus making manual tracking extremely difficult.

According to Landing AI CEO and Founder Andrew Ng, the technology was developed in response to requests by their clients, which includes Foxconn, the main manufacturer of Apple's prized iPhones.

The company also says that this technology can be integrated into existing surveillance cameras. However, it's still exploring ways in which to alert people when they get too close to each other. One possible method is the use of an audible alarm that rings when individuals breach the minimum distance required with other people.

According to Reuters, Amazon already uses a similar machine-learning tool to monitor its employees in their warehouses. In the name of COVID-19 mitigation, companies around the world are grabbing whatever machine-learning AI tools they can get in order to surveil their employees. A lot of these tools tend to be cheap, off-the-shelf iterations that allow employers to watch their employees and listen to phone calls as well.

Landing AI insists that their tool is only for use in work settings, even including a little disclaimer that reads "The rise of computer vision has opened up important questions about privacy and individual rights; our current system does not recognize individuals, and we urge anyone using such a system to do so with transparency and only with informed consent."

Whether companies that make use of this tool adhere to that, we'll never really know.

But we definitely don't want Big Brother to be watching our every move.

Cover image sourced from New Straits Times / AFP.

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This AI tool uses machine learning to detect whether people are social distancing properly - Mashable SE Asia

Machine learning insight will lead to greener and cheaper mobile phone towers – University of Southampton

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Published:27 April 2020

Off-grid renewable energy solutions will be introduced to mobile telecom towers in developing countries through a new collaboration involving researchers at the University of Southampton.

London-based Global Tower Solutions will work with machine learning experts in Electronics and Computer Science on the new project funded by the national SPRINT business support programme.

The partnership will develop a solution that is estimated to cost around half that of existing diesel generators, while also improving access to mobile communication services in targeted countries in Asia and sub-Saharan Africa.

Professor Gopal Ramchurn, Director of the Universitys Centre for Machine Intelligence, said: Mobile phone towers make a significant contribution to CO2 emissions and Global Tower Solutions is looking to decrease carbon emissions through a reduction in diesel powered mobile phone towers.

Through the SPRINT project, the University will apply machine learning techniques to high- and low-resolution datasets, drone imagery, census data, data from satellite images and other data available around settlements. This will help to define the business case for renewable energy for phone towers which can then be delivered to mobile phone operators to identify the most appropriate renewable energy sources and which regions need mobile communications the most.

Mobile communication has been shown to be a key factor in relieving poverty by providing access to information and financial services that drive trade, education, reduction in poverty and better health. The project will also lead to the reduced use of diesel and improved sustainability of small businesses that underpin developing economies.

Mark Eastwood, Chief Executive Officer of Global Tower Solutions, said: The renewable energy market has evolved over last 10-12 years and we set the company up 3-4 years ago with the aim of moving from diesel generation towards solar power and storage. We wanted to remove the diesel generation price point using sustainable, non-polluting storage solutions, particularly in emerging markets.

The SPRINT project will help us to explore the impact of renewable generating assets on both telco tower businesses and local communities, using business insights from datasets. Working with the University of Southampton, we can access expertise that can support us in high precision localised intelligence including valuable business insights, topological mapping, individual patterns of usage and movement of local population.

SPRINT (SPace Research and Innovation Network for Technology) helps businesses through the commercial exploitation of space data and technologies. The 4.8m programme provides unprecedented access to university space expertise and facilities. Southampton researchers are contributing to several SPRINT projects, including a recently announced collaboration with Smallspark Space Systems that is using AI to optimise aerostructure designs.

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Machine learning insight will lead to greener and cheaper mobile phone towers - University of Southampton

Trump betting millions to lay the groundwork for quantum internet in the US – CNBC

In the 1960s the U.S. government funded a series of experiments developing techniques to shuttle information from one computer to another. Devices in single labs sprouted connections, then neighboring labs linked up. Soon the network had blossomed between research institutions across the country, setting down the roots of what would become the internet and transforming forever how people use information. Now, 60 years later, the Department of Energy is aiming to do it again.

The Trump administration's 2021 budget request currently under consideration by Congress proposes slashing the overall funding for scientific research by nearly 10% but boosts spending on quantum information science by about 20%, to $237 million. Of that, the DOE has requested $25 million to accelerate the development of a quantum internet. Such a network would leverage the counterintuitive behavior of nature's particles to manipulate and share information in entirely new ways, with the potential to reinvent fields including cybersecurity and material science.

Whilethetraditional internet for general useisn't going anywhere, a quantum networkwouldoffer decisive advantages for certain applications: Researchers could use it to develop drugs and materials by simulating atomic behavior onnetworked quantum computers, for instance, and financial institutions and governments would benefit from next-level cybersecurity. Many countries are pursuing quantum research programs, and with the 2021 budget proposal, the Trumpadministration seeks to ramp up thateffort.

"That level of funding will enable us to begin to develop the groundwork for sophisticated, practical and high-impact quantum networks," says David Awschalom, a quantum engineer at the University of Chicago. "It's significant and extremely important."

A quantum internet will develop in fits and starts, much like the traditional internet did and continues to do. China has already realized an early application, quantum encryption, between certain cities, but fully quantum networks spanning entire countries will take decades, experts say. Building it willrequire re-engineering the quantum equivalent of routers, hard drives, and computers from the ground up foundational work already under way today.

Where the modern internet traffics in bits streaming between classical computers (a category that now includes smart phones, tablets, speakers and thermostats), a quantum internet would carry a fundamentally different unit of information known as the quantum bit, or qubit.

Bits all boil down to instances of nature's simplest eventsquestions with yes or no answers. Computer chips process cat videos by stopping some electric currents while letting others flow. Hard drives store documents by locking magnets in either the up or down position.

Qubits represent a different language altogether, one based on the behavior of atoms, electrons, and other particles, objects governed by the bizarre rules of quantum mechanics. These objects lead more fluid and uncertain lives than their strait-laced counterparts in classical computing. A hard drive magnet must always point up or down, for instance, but an electron's direction is unknowable until measured. More precisely, the electron behaves in such a way that describing its orientation requires a more complex concept known as superposition that goes beyond the straightforward labels of "up" or "down."

Quantum particles can also be yoked together in a relationship called entanglement, such as when two photons (light particles) shine from the same source. Pairs of entangled particles share an intimate bond akin to the relationship between the two faces of a coin when one face shows heads the other displays tails. Unlike a coin, however, entangled particles can travel far from each other and maintain their connection.

Quantum information science unites these and other phenomena, promising a novel, richer way to process information analogous to moving from 2-D to 3-D graphics, or learning to calculate with decimals instead of just whole numbers. Quantum devices fluent in nature's native tongue could, for instance, supercharge scientists' ability to design materials and drugs by emulating new atomic structures without having to test their properties in the lab. Entanglement, a delicate link destroyed by external tampering, could guarantee that connections between devices remain private.

But such miracles remain years to decades away. Both superposition and entanglement are fragile states most easily maintained at frigid temperatures in machines kept perfectly isolated from the chaos of the outside world. And as quantum computer scientists search for ways to extend their control over greater numbers of finicky particles, quantum internet researchers are developing the technologies required to link those collections of particles together.

The interior of a quantum computer prototype developed by IBM. While various groups race to build quantum computers, Department of Energy researchers seek ways to link them together.

IBM

Just as it did in the 1960s, the DOE is again sowing the seeds for a future network at its national labs. Beneath the suburbs of western Chicago lie 52 miles of optical fiber extending in two loops from Argonne National Laboratory. Early this year, Awschalom oversaw the system's first successful experiments. "We created entangled states of light," he says, "and tried to use that as a vehicle to test how entanglement works in the real world not in a lab going underneath the tollways of Illinois."

Daily temperature swings cause the wires to shrink by dozens of feet, for instance, requiring careful adjustment in the timing of the pulses to compensate. This summer the team plans to extend their network with another node, bringing the neighboring Fermi National Accelerator Laboratory into the quantum fold.

Similar experiments are under way on the East Coast, too, where researchers have sent entangled photons over fiber-optic cables connecting Brookhaven National Laboratory in New York with Stony Brook University, a distance of about 11 miles. Brookhaven scientists are also testing the wireless transmission of entangled photons over a similar distance through the air. While this technique requires fair weather, according to Kerstin Kleese van Dam, the director of Brookhaven's computational science initiative, it could someday complement networks of fiber-optic cables. "We just want to keep our options open," she says.

Such sending and receiving of entangled photons represent the equivalent of quantum routers, but next researchers need a quantum hard drive a way to save the information they're exchanging. "What we're on the cusp of doing," Kleese van Dam says, "is entangled memories over miles."

When photons carry information in from the network, quantum memory will store those qubits in the form of entangled atoms, much as current hard drives use flipped magnets to hold bits. Awschalom expects the Argonne and University of Chicago groups to have working quantum memories this summer, around the same time they expand their network to Fermilab, at which point it will span 100 miles.

But that's about as far as light can travel before growing too dim to read. Before they can grow their networks any larger, researchers will need to invent a quantum repeater a device that boosts an atrophied signal for another 100-mile journey. Classical internet repeaters just copy the information and send out a new pulse of light, but that process breaks entanglement (a feature that makes quantum communications secure from eavesdroppers). Instead, Awschalom says, researchers have come up with a scheme to amplify the quantum signal by shuffling it into other forms without ever reading it directly. "We have some prototype quantum repeaters currently running. They're not good enough," he says, "but we're learning a lot."

Department of Energy Under Secretary for Science Paul M. Dabbar (left) sends a pair of entangled photons along the quantum loop. Also shown are Argonne scientist David Awschalom (center) and Argonne Laboratory Director Paul Kearns.

Argonne National Laboratory

And if Congress approves the quantum information science line in the 2021 budget, researchers like Awschalom and Kleese van Dam will learn a lot more. Additional funding for their experiments could lay the foundations for someday extending their local links into a country-wide network. "There's a long-term vision to connect all the national labs, coast to coast," says Paul Dabbar, the DOE's Under Secretary for Science.

In some senses the U.S. trails other countries in quantum networking. China, for example, has completed a 1,200-mile backbone linking Beijing and Shanghai that banks and other companies are already using for nearly perfectly secure encryption. But the race for a fully featured quantum internet is more marathon than sprint, and China has passed only the first milestone. Kleese van Dam points out that without quantum repeaters, this network relies on a few dozen "trusted" nodes Achilles' heels that temporarily put the quantum magic on pause while the qubits are shoved through bit-based bottlenecks. She's holding out for truly secure end-to-end communication. "What we're planning to do goes way beyond what China is doing," she says.

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Researchers ultimately envision a whole quantum ecosystem of computers, memories, and repeaters all speaking the same language of superposition and entanglement, with nary a bit in sight. "It's like a big stew where everything has to be kept quantum mechanical," Awschalom says. "You don't want to go to the classical world at all."

After immediate applications such as unbreakable encryptions, he speculates that such a network could also lead to seismic sensors capable of logging the vibration of the planet at the atomic level, but says that the biggest consequences will likely be the ones no one sees coming. He compares the current state of the field to when electrical engineers developed the first transistors and initially used them to improve hearing aids, completely unaware that they were setting off down a path that would someday bring social media and video conferencing.

As researchers at Brookhaven, Argonne, and many other institutions tinker with the quantum equivalent of transistors, but they can't help but wonder what the quantum analog of video chat will be. "It's clear there's a lot of promise. It's going to move quickly," Awschalom says. "But the most exciting part is that we don't know exactly where it's going to go."

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Trump betting millions to lay the groundwork for quantum internet in the US - CNBC