Monthly Archives: October 2019

This is an edition ofQuintessence, a series about fundamental ideas in science.

Earlier this year, IBM unveiled a much-hyped device at the Consumer Electronics Show in Las Vegas: a black cylinder hanging at the centre of a locked glass cage. Light from a wide panel on top bounced off of it. It was a simulacrum of a computing machine but it looked futuristic, even too futuristic. It didnt look like any normal computer because it wasnt.

It is easy to forget that even the simplest function on any electronic device in use today is the result of many, many transistors working in tandem. A keystroke, a button press, a single tap all of it is about current flowing in and out of a transistor, the modern version of which was invented 72 years ago.

A bit, which is the most fundamental unit of computing, refers to the state of a transistor. If the transistor is on, the bit has a value of 1; if the transistor is off, the bit has a value of 0. Every instruction fed to a computer and output derived from it is a pattern of such 0s and 1s. The computers hardware and software manipulate these patterns using a set of rules called Boolean algebra. All this logic flows at blazing speeds, enabling humankind to make rapid technological advancements.

But even though transistors have become incredibly sophisticated over the years and engineers have become able to cram billions of them on tinier and tinier chips, they are still classical objects. The kind of computing they lend themselves to uses the simpler principles of classical physics and is therefore limited by the limitations of these principles. The deepest of them is this: transistors can either be on or off. A bit can only assume one value at a time.

This is where IBMs sleek machine turns the curve. Enclosed in the black cylinder lies the soul of a quantum computer, in the form of a chip that taps into a more esoteric set of computing possibilities.

Also read:A Walk With Steven Kivelson Through the Realm of Strange Materials

Quantum computers are devices that manipulate quantum bits, or qubits. But instead of using a transistor to perform this function, quantum computers directly encode this information onto elementary particles like electrons and photons, or even entire atoms. These particles are thus part of a quantum computers hardware, and because they play by quantum rules, they execute their functions as qubits through strange quantum mechanical effects.

One of these effects is superposition. Where a classical bit has two states 0 and 1 a qubit also has a state where it is neither 0 nor 1 (but not a third value). This value is a fuzzy combination of two states, like 40% 1 and 60% 0. That is, if a classical bit is like a mechanical switch that can be turned on or off, a qubit can be on and off at the same time but neither completely on or off. Classical objects cannot be in such superposition.

Most people have already heard of such behaviour in the form of the Schrdingers cat thought experiment. A cat and a bowl of poison are placed in a sealed box. Until an observer opens the box and checks, the cat a metaphor for subatomic particles is to be considered both dead and alive. Similarly, an unobserved electron can have a spin pointing up and down at the same time, but the moment it is observed, it defaults to one of the two states: either up or down.

Next, two classical bits can have one of the following four configurations: {0, 0}, {1, 1}, {1, 0} and {0, 1}. But two qubits can exist in a mixture of all four at the same time; indeed, N number of qubits can exist as a simultaneous mixture of 2N states, each one representing a possible solution. Put differently, instead of tackling a problem by pursuing one solution at a time like a classical computer, a quantum computer can pursue multiple potential solutions at once and, presumably, arrive at the optimal one faster.

Superposition explains how a single qubit can be more powerful than a single bit. To understand how multiple qubits can work together as computers will require physicists use another concept called entanglement.

Quantum entanglement establishes strong ties between particles such that if one particle changes in a particular way, the other one also changes in a corresponding way. For example, if two qubits are entangled and one of them reveals its state, the other qubit automatically reveals its state as well.

Though the components of a quantum computers are markedly different from those of a classical computer, they still have to behave like computers, including processing instructions and producing an answer to a question in a predictable amount of time. This means a set of entangled qubits in superposition should ultimately collapse into a meaningful configuration of 1s and 0s on demand.

This is tricky. A qubit has a finite probability of existing in one of two quantum states. So N qubits have a tendency to randomly settle into any one of the 2N possible states when measured. Eight qubits, for example, could settle into one of 256 states. To get around this problem, the qubits have to be subtly manipulated to increase their probability of chasing the correct, or more desirable, paths.

Scientists achieve this by orchestrating the qubits in such a way that the signatures of the undesirable quantum states cancel out and the right ones add up. This is how some quantum computing algorithms that can take advantage of this technique based on the idea of interference from high-school physics vastly outperform their classical counterparts.

Also read:The DIY Experiment That Captures All the Mystery of Quantum Physics

For example, a quantum computer could use Grovers algorithm named for the computer scientist Lov Kumar Grover to sift through very large, unstructured databases to find a specific entry faster than a classical machine can. Using Peter Shors algorithm, a quantum computer can find the factors of large integers that are prime numbers much faster than the best algorithms classical computers use.

Scientists in various fields would also like to understand how complex molecules behave and interact with each other. Powerful supercomputers struggle with the dynamics of such many-body interactions, but quantum computers are expected to have a knack for them because theyre networks of particles themselves.

On the flip side, quantum computers arent always better. There are many problems for which classical computers arent efficient and quantum computers arent either. In most of these cases, increasing the scale of the problem exponentially increases the amount of time the computer needs to find the answer. Quantum computers might be able to crack some of them efficiently but not some others.

It would also be prudent to move our eyes away from the horizon of infinite possibilities and towards the mountain range standing in the way. Qubits must be stable and work well together for a computer to compute. This is easier said than done. Multiple qubits in a superposition, and entangled with each other, tend to be quite fragile. Even the smallest physical vibration can destroy their collective coherence, interfere with their quantum nature and induce large errors that can render the machine useless.

Different research groups around the world have stretched engineering to its bleeding edge to prevent such decoherence. IBMs monolithic quantum computer cools its superconducting chip which carries the qubits to about 0.01 K, or 270-times colder than outer space.

Late last month, a paper quietly appeared on and promptly disappeared from a NASA website. In the paper, scientists from Google claimed to have performed a computing task way out of reach of even the best conventional computers using a quantum computer.

The company hasnt issued an official comment or shared a peer-reviewed paper. According to various news reports, its 53-qubit machine performed a purpose-built task a computation in about 200 seconds when a powerful classical machine would have required millennia. If independent experts are able validate Googles claim, it will be the first time a quantum computer will have surpassed a classical machine at a specific task.

That said, we are still decades away from a practically useful quantum computer. The transistor reigns supreme for now.

The author would like to thank Vedangi Pathak and Kevin Dsouza for discussions about quantum mechanics and computing.

Ronak Guptais doing a PhD in fluid mechanics at the University of British Columbia, Vancouver.

View post:

What on Earth Is a Quantum Computer, and Why Should You Care? - The Wire

It's been a while now and Goldman Sachs still doesn't seem to have anyone to lead its proposed 'quantum computing research' team.

The firm doesn't date stamp its jobs, but we first noticed it was advertising for a 'Leader of a new quantum computing research team in R&D Engineering,' in early September. One month on, there don't seem to have been any takers: the job is still open.

Goldman didn't respond to a query on the role. It might be simply that the firm is taking its time and interviewing every quantum computing expert on the market. Or it might just be that quantumpeople are hard to come by.

Goldman's requirements are fairly precise: it wants someone who can identify applications for quantum computing across Goldman, who can talk to clients about quantum computing, who can liaise with Goldman staff and academics,and who can lead thenew quantum computingresearch team. The ideal would seem to be a quantum computingPhDwith client facing skills, which might be hard to come by.

In the meantime, Goldman has at least one quantum computing research already.Rajiv Krishnakumar joined the firm in March 2018 and began work in the Franchise Analytics Strategy and Technology (FAST) team, which is tasked with applying machine learning across the firm.Krishnakumar moved into the quantum computing research team as an associate last month. It probably helps that he has a PhD in applied physics from Stanford and that he spent six months as a postdoctoral research fellow in machine learning at Caltech.

Goldman Sachs seems to be unusual among banks in having a dedicated quantum computing research team. Others are certainly interested though -Roland Fejfar, head of technology business development at Morgan Stanley for EMEA and APAC, says on LinkedIn that one of his tasks is to look at 'disruptive technology' like quantum systems. And JPMorgan hired a quantum research scientist on a salary of $150k in New York in March 2019, according to the H1B Visa database.

Google claimed last month that it had built a quantum computer that could perform a calculation in three minutes and 20 seconds instead of the 10,000 years it would take the fastest traditional computer. When and if quantum computers become usable, experts have warned that all existing systems of encryption will become instantly meaningless.

Google is also looking for quantum recruiting talent. It's currently advertising nine roles, seven of which are related to quantum artificial intelligence (AI). All are in California. Goldman, by comparison, wants its quantum researchers in New York City - which may be a harder sell.

Have a confidential story, tip, or comment youd like to share? Contact: sbutcher@efinancialcareers.com in the first instance. Whatsapp/Signal/Telegram also available.

Bear with us if you leave a comment at the bottom of this article: all our comments are moderated by human beings. Sometimes these humans might be asleep, or away from their desks, so it may take a while for your comment to appear. Eventually it will unless its offensive or libelous (in which case it wont.)

Read this article:

Goldman Sachs can't seem to find anyone to lead its quantum computing team - eFinancialCareers

Todays conventional computers may soon reach their limits. Moores law, which predicts that it takes about two years to double the power of computers, is expected to reach a dead end soon. Dealing with more complex problems ahead needs quantum computers, where individual atoms store and process information.Serge Haroche,a French physicist who won the Nobel Prize in 2012 for his work on manipulating individual quantum systems, discussed the subject withShimona Kanwarduring a recent visit to India.

August saw an agreement between India and France on cooperation in the fields of quantum computing. Where do you see this association headed?

I think laboratory scale classical computing is a matter of technology and one can see in what direction it is going.Quantum computing is still a question of basic science. So, you cannot predict if and when it will lead to practical applications. There is big progress in quantum computing, quantum communication and quantum meteorology using quantum devices. There are laboratories working on these in India and France.

One way to combat the quantum decoherence is supercooling, but its impractical for commercialisation. What kind of solution will emerge?

There are methods called quantum error correction to combat decoherence. This works on paper but not to the level of precision which is required to get a quantum computer. It means if you get a practical device, it will be very cold and not like a laptop which everybody can have at home. There are lot of challenges to scale up to a size which is useful. There is lot of hype in the field of quantum devices. Many private companies which are involved in this want to make profit. Quantum computing is trapped between hype and hope. A hope that one day it will lead to something useful. But as always in science you get surprises and what will be useful is may be not what we expect now.It is very rare in science where you have a path which leads you to a discovery that is predicted 20-30 years ahead of time. For the time being, quantum computing is basic science and not applied yet. Those who promise quantum computers are overselling, I think.

Why are researchers not motivated to pursue basic science?

I think basic science is background. You cannot have applied if there is no basic. Basic science requires a lot of time. Before it gives us application, it takes a very long time. For instance, the first idea of the laser wasgiven by Einstein in 1916 and the first laser came up in 1960. It took 44 years between the basic discovery and the invention. Sometimes it takes less time, but on an average 10-20 years. The big problem we have as scientists is to make sure that people will give us money and keep patience, and not ask for short term results. You need to build an atmosphere of trust and give time to basic science to develop.I think one of the problems is that there are not many positions in basic science. If you do not nourish basic science, you will not have good applied science.

Can we have Chinas model where there is huge investment in science?

China invests a huge amount and has made advances in science in the last 20 years. They have very good science institutes. They do good science in physics and biology. China has one problem and that is lack of freedom. I think science cannot be disconnected from humanities. A good scientist needs to have freedom of soul, freedom to choose his topic and to work with passion. A good scientist is driven by his/her own curiosity. I am sure if China gives more freedom to its researchers, it will be much more productive than it is now.

CERN (European Organisation for Nuclear Research) has been a successful example of science diplomacy. What advice would it give?

CERN is a very big project as it involves thousands of researchers from all over the world. This is necessary as it is a project in high energy physics and requires huge instruments like big accelerators which no single country can manage. However, my area of working is in small scale physics. As a policy, small-scale physics is very interesting as it brings PhD students hands on with physics and they are responsible for their own project.So, it is a very different way of training people. I think for a country like India, this small-scale physics is good becauseit allows it to develop science in different institutes.A big project like CERN or space agency projects are interesting for governments as it gives them bigger media attention. I think the work you do on smaller scale, even if it does not attract attention, is more fruitful.

DISCLAIMER : Views expressed above are the author's own.

Read the original:

'Quantum computing's trapped between hype and hope in science you get surprises and what's useful may not be what we expect now' - Times of India

This book shows how our understanding of quantum physics is mostly, but not entirely, correct

Cover: Six Impossible Things by John Gribbin(Credit: Icon Books Ltd, 2019).

Quantum physics is so bizarre that even physicists dont understand it. According to the rules of quantum physics, a cat can be alive and dead at the same time, an electron can be in two places simultaneously, and a subatomic particle can also be a wave. But nobody has ever been able to provide a rational explanation for how these contradictory and seemingly impossible things can be true. Basically, the subatomic world is so strange that even Einstein could only shrug and describe it as spooky action at a distance.

Undaunted, British science writer and astrophysicist John Gribbin wrote a small book, Six Impossible Things (Icon Books; 2019: Amazon US / Amazon UK) that succinctly summarizes six of the foremost hypotheses that seek to explain the mysteries of the subatomic world. This slim hardcover is an elegant and accessible attempt to explain quantum physics to the nonspecialist, and is one of the six books included on the shortlist for the Royal Society Insight Investment popular Science Book Prize for 2019.

Six of the books ten chapters correspond to each of the six most popular explanations that may underlie quantum physics weirdness: All of them are crazy, and some are more crazy than others, Dr. Gribbin notes early in his book, but in this world crazy does not necessarily mean wrong, and being more crazy does not necessarily mean more wrong (p. xvii). The six impossible things include the Copenhagen Interpretation, the Pilot Wave Interpretation, the Many Worlds Interpretation, the Decoherence Interpretation, the Ensemble Non-Interpretation (also known as the Statistical Interpretation), and the Timeless Transactional Interpretation.

The chapters are quite short (as is the book itself), and include black-and-white diagrams and images of the main proponents of each hypothesis along with a condensed and readable description of that hypothesis. Professor Gribbins coherent summary of each hypothesis makes these incredibly complex ideas, puzzled over by physics leading minds since the late 1920s, into something that may be vaguely comprehensible to most readers, even if these ideas remain firmly embedded in the realm of the fantastic for physicists as well as for mere mortals.

Beside the fact that simply reading this book gave me incredible dreams, I was most tantalized during my waking hours by Dr. Gribbins too brief description of quantum computing, a fascinating technological advancement that cannot come too soon, in my opinion. But more interesting even than quantum computing (and its concomitant cybersecurity) is coming to a firm understanding about the reason that it works and that remains elusive.

The book lacks maths, so if you are skittish about mathematics, or lack a maths background, you will breathe a sigh of relief to discover that this book is still quite comprehensible well, as intelligible as quantum physics can be made.

If quantum physics still leaves you breathless, Professor Gribbin also summarizes each of these six impossible interpretations in a neat, and amusing, single sentence. Highly recommended for students of the sciences and fans of science fiction, as well as for anyone who is curious to understand the strange world of quantum physics.

Read the rest here:

Six Impossible Things: The Mysteries Of The Subatomic World - Forbes

The Creative Destruction Lab (CDL) at the University of Torontos Rotman School of Management is pleased to announce a technology collaboration with IBM Q to drive quantum education opportunities with members of the Ventures in CDLs Quantum Incubator Stream. As part of this collaboration CDL will receive access and hands-on technical support for the public IBM Q Experience systems, and IBMs open source quantum software platform, Qiskit. IBM will provide ongoing support through a single point of contact coordinator.

The CDL Quantum Stream has been a neutral testing ground, offering our startups access to technical expertise and computing options available across the industry, says Peter Wittek, Academic Director of the CDL Quantum stream and Assistant Professor at the University of Toronto. The IBM Q collaboration further underlines our dedication to raising the quantum ecosystem as a whole, as well as adding the tremendous amount of support that the IBM Q teams expertise comes with.

Collaborations with organizations like CDL are essential, as more and more startups, corporations, academic institutions and research labs begin exploring the possibilities of quantum computing and work to get quantum ready, said Anthony Annunziata, Global Leader, IBM Q Network. Were looking forward to working with CDLs Quantum Incubator Stream startups.

The Quantum Stream at CDL-Toronto brings together entrepreneurs, investors, AI experts, leading quantum information researchers, and quantum hardware companies to build ventures in the nascent domain of quantum machine learning and quantum optimization. CDL Quantum will provide IBM Q Experience and Qiskit education as part of its robust set of resources for founders to launch and scale a startup, including:

Mentorship from experienced entrepreneurs who have experience scaling quantum-based companies whether as an entrepreneur or early investor. Technical validation and feedback from leading academic researchers such as Seth Lloyd (quantum information and complex systems), Roger Melko (quantum many-body physics), Michele Mosca (quantum algorithms and cryptography), and Peter Wittek (quantum machine learning). Guidance on strategy and business development from Rotman School of Management faculty members. Business development support from top students at the Rotman School of Management (University of Toronto) Opportunities to raise capital from angel investors and top-tier venture capital firms IBM Q joins CDL Quantums three technology partners that provide quantum computing resources to participating ventures in the program: D-Wave Systems, Rigetti Computing and Xanadu.

See the article here:

Creative Destruction Lab collaborates with IBM Q to train startups in quantum computing - Quantaneo, the Quantum Computing Source

On a new episode of The Joe Rogan Experience, actress and comedian Roseanne Barr tells host Joe Rogan that traditional forms of money will become a thing of the past.

According to Barr,

Theres not gonna be any more money.

Rogan then wonders if theres going to be any more cryptocurrencies, noting recent advancements from Google in the field of quantum computing.

Such developments could potentially lead to a device thats powerful enough to crack modern forms of encryption spelling trouble for Bitcoin and the thousands of other cryptocurrencies that are based on blockchain technology.

Says Rogan,

This Google quantum computing thing they think its a huge threat to cryptocurrency.

Im too stupid to understand whether or not theyre right. Im way too uninformed too. But what theyre trying to say is there would be no way you would be able to encode or encrypt this information that would keep it from this insane computing power theyre developing.

Some analysts, however, caution that Google is far away from breaking Bitcoin and note that research on how to protect the internet and blockchain technology against the forces of quantum computing is well underway.

Digital money pioneer David Chaum, a cryptographer who developed the anonymous digital currency eCash in 1983, is designing his own digital asset called Praxxis that he says will feature anonymous transactions and quantum resistance.

QAN is another project focused on creating a blockchain that can resistant a potential quantum threat.

The companys chief technology officer Johann Polecsak tells Bitcoin.com,

The notion of Google achieving a quantum breakthrough sounds very dramatic, but in reality, its hard to gauge the significance at this time.

How can we be sure that Googles quantum computer is more powerful than D-waves, for example, which surpassed 1,000 qubits four years ago?

Dragos Ilie, a quantum computing and encryption researcher at Imperial College London, says Google is far from cracking the cryptographic algorithms utilized by Bitcoin.

Googles supercomputer currently has 53 qubits.

In order to have any effect on bitcoin or most other financial systems, it would take at least about 1500 qubits and the system must allow for the entanglement of all of them

As you add more qubits the system becomes more and more unstable [though] researchers can try different approaches for solving these issues so maybe there are ways to mitigate these problems but right now we are quite far from breaking Bitcoin.

Read more from the original source:

Joe Rogan Ponders Googles Quantum Breakthrough, Says It Could Be Huge Threat to Bitcoin and Crypto - The Daily Hodl

A University of Toronto quantum computing assistant professor is missing in India after an avalanche struck during a climbing trip.

Peter Wittek, 37, was resting in his tent Sunday when the avalanche hit, his brother Gergo Oberfrank told CTVNews.ca in a phone call from Budapest.

Wittek and a group of five others from Singapore, Mauritius, Vietnam and Hungary were hiking Mount Trisul in the Himalayas. One other member of the group was hit by the avalanche, the team manager told Oberfrank, but managed to dig out from the snow.

Only my brother is missing, he said. My brother was in the worst place that was possible in that moment.

A search was underway, including the use of helicopters, but poor weather conditions have hampered efforts.

Wittek, who moved to Toronto last year, has extensive experience as a mountaineer. Personal and professional friend Tomas Babej said that Wittek had trained for months for the specific trip, including taking mountaineering courses. His work in quantum physics has brought Wittek renown in Toronto where he is an assistant professor in the Rotman School of Management and where he wrote the first seminal book written on quantum machine learning, said Babej on the phone from Toronto.

He is a fundamental cornerstone in this new emerging field of quantum machine learning, he said. Wittek is an adviser for Babejs company and countless other startups in the field, he added. I dont know how many of these things will continue if hes not to be found.

A spokesperson for the University of Torontos Rotman School of Management confirmed the news to CTVNews.ca. We are in touch with his family and continue to monitor the situation actively, wrote Ken McGuffin in an email.

The Mount Trisul area of the Himalayas that Wittek was climbing was described as highly avalanche-prone by Swati Bhadoriya, district magistrate of Chamoli, in an interview with local media. "This is considered one of the toughest peaks in the world. It requires a lot of expertise," he told the India bureau of the Straits Times.

Friends and family are optimistic that the search efforts can continue tomorrow in India.

The rescue teams are really experienced and well-trained. They will try to get their best men to find my brother, said Oberfrank.

Everybody loves Peter. The world is going to be a worse place without him. He is the best friend I have ever known. He is my ultimate role model.

Here is the original post:

University of Toronto prof missing in India after avalanche - CTV News

DUBLIN, Oct. 8, 2019 /PRNewswire/ -- The "Exascale Computing Market by Hardware, Software, Services, and Industry Vertical 2019 - 2024" report has been added to ResearchAndMarkets.com's offering.

Exascale computing takes High Performance Computing (HPC) to a level of computational ability in the range of quintillion calculations per second. This degree of performance is necessary to solve very complex problems such as climate research, oil and gas exploration, molecular modeling, and physical simulations. Accordingly, the exascale computing market is currently driven by demands by a few business and governmental entities that require intensive number crunching for very specific problems.

Well-suited for applications that require high performance data analysis, the exascale computing market supports a variety of apps such as high frequency trading, autonomous vehicles, and genomics-based personalized medicine, computer-aided design, and deep learning. Some specific solution areas within science and technology include computational fluid dynamics, advanced simulations, complex system modeling, and advanced imaging analysis such as seismic tomography. Most of the initial applications have been within the realm of large corporations and government funded initiatives.

With the goal to launch a USA led exascale ecosystem by 2021, the DOE driven Exascale Computing Project is looking to increase computing power by 50 times current system capabilities. This project is intended to tip the scales in favor of the United States in terms of economic competitiveness as well as national security by enabling breakthroughs in scientific discovery and technological innovation such as cybersecurity. Accordingly, the National Nuclear Security Administration is one of the partners in this initiative.

While traditionally within the domain of government and large corporations, the exascale computing market will become increasingly more accessible to a broad range of companies with a corresponding wider range of applications. For example, exascale-level processing will be a critical component for processing data from millions of self-driving vehicles as part of an intelligent transportation system that optimizes traffic flow and utilization of vehicular resources. The exascale computing market will also become increasingly available to small businesses and engineering teams as artificial intelligence is leveraged to help engineers better leverage supercomputers.

Exascale-level computing is anticipated to become more mass market as computing costs continue to decrease and flexible deployment options are introduced. This research anticipates that the Exascale Computing Market will expand dramatically as cloud-based solutions are implemented to allow for HPC as a Service (HPCaaS). Small to medium sized businesses will benefit greatly from the HPCaaS model as they may utilize exascale-level computing in an on-demand basis for the duration of a project. Exascale-level HPC will be very important for certain consumer-oriented applications and services.

This report evaluates the exascale computing market including companies, solutions, use cases, and applications. It evaluates the exascale computing market by component, hardware type, service type, and industry verticals with forecasting from 2019 to 2024. The report also provides analysis of leading companies in the HPC space including those focused on developing exascale-level computing solutions.

Select Report Findings:

Key Topics Covered:

1. Executive Summary

2. Introduction

2.1 Next Generation Computing

2.2 High Performance Computing

2.3 Exascale Computing

2.3.1 Supercomputers

2.3.2 Exascale Computing Development

2.3.3 Exascale Use Cases and Application Areas

2.3.4 Regulatory Framework

2.3.5 Value Chain Analysis

3. Exascale Computing Market Analysis and Forecasts 2019 - 2024

3.1.1 Exascale Computing Market by Component 2019 - 2024

3.1.2 Exascale Computing Market by Hardware Type 2019 - 2024

3.1.3 Exascale Computing Market by Service Type 2019 - 2024

3.1.4 Exascale Computing Market by Industry Vertical 2019 - 2024

4. Company Analysis

4.1 Vendor Ecosystem

4.2 Leading Companies

4.2.1 Amazon Web Services Inc.

4.2.2 Atos SE

1.1.1 Advanced Micro Devices Inc.

1.1.2 Cisco Systems

4.2.3 DELL Technologies Inc.

4.2.4 Fujitsu Ltd

4.2.5 Hewlett Packard Enterprise

4.2.6 IBM Corporation

4.2.7 Intel Corporation

4.2.8 Microsoft Corporation

4.2.9 NEC Corporation

4.2.10 NVIDIA

4.2.11 Rackspace Inc.

5. Conclusions and Recommendations

6. Appendix: Alternative to Classical Exascale - Quantum Computing

Companies Mentioned

For more information about this report visit https://www.researchandmarkets.com/r/5bxbmh

Research and Markets also offers Custom Research services providing focused, comprehensive and tailored research.

Media Contact:

Research and Markets Laura Wood, Senior Manager press@researchandmarkets.com

For E.S.T Office Hours Call +1-917-300-0470 For U.S./CAN Toll Free Call +1-800-526-8630 For GMT Office Hours Call +353-1-416-8900

U.S. Fax: 646-607-1907 Fax (outside U.S.): +353-1-481-1716

SOURCE Research and Markets

http://www.researchandmarkets.com

Link:

Exascale Computing Market by Hardware, Software, Services, and Industry Vertical 2019-2024: The $2.4B Global Exascale Computing Market is Expanding to...

The artificial intelligence (AI) space is evolving at a rapid pace and part of that evolution includes how humans interact with computers, also known as conversational AI. The Global X Future Analytics Tech ETF (NasdaqGM: AIQ) is one ETF that provides exposure to that theme.

The Global X Future Analytics Tech ETF tries to reflect the performance of the Indxx Artificial Intelligence & Big Data Index, which is comprised of companies involved in the development and utilization of artificial intelligence and big data. The underlying index includes those involved in generating vast amounts of data and developing proprietary AI systems to derive actionable insights from the data set. The ETF will also cover companies that provide AI-as-a-Service for big data analytics or are developing hardware integral to powering AI systems, such as quantum computing.

Through personal assistants, such as Apples Siri and Amazons Alexa, humans are already engaged in some level of conversational AI, but the industry is looking to enhance those interactions and more.

Conversational AI is the development of software programs that allow a computer to understand what a human intends to say or ask for, decipher its meaning, and communicate relevant responses, said Global X in a recent note. Given the complexities of human interactions, AI algorithms depend on language models that are massive in scope and complexity and backed by substantial computing power.

AIQs underlying index screens artificial intelligence companies based on artificial intelligence applied to product services and artificial intelligence-as-a-service for big data applications. Additionally, companies included in the underlying index include companies that produce hardware for artificial intelligence applications and those developing quantum computing.

Natural language understanding (NLU), a theme some AIQ components tap into, is viewed as a driver of conversational AI advancements.

NLU is one branch of AI that leverages computing power to understand language inputs, either speech, text, or a combination of both. For NLU technology to be maximally useful, it must be able to process language in a way that is not exclusive to a single task, genre, or dataset, according to Global X.

Related:Make The Move to Quality With This Global X Dividend ETF

AIQ holds 80 stocks and as is the case with many thematic ETFs, the fund is not explicitly dedicated to one sector. With AIs wide-ranging applications, multiple sectors can be part of the AI investment thesis, but the fund features hefty allocations to the technology and communication services sectors.

Conversational AI is expected to be omnichannel, multi-device, and multi-language, potentially disrupting the nearly $86 billion global outsourced services market by leveraging chatbots, messaging apps, digital/personal assistants, and voice search, notes Global X.

For more thematic investment strategies, visit our Thematic Investing Channel.

The opinions and forecasts expressed herein are solely those of Tom Lydon, and may not actually come to pass. Information on this site should not be used or construed as an offer to sell, a solicitation of an offer to buy, or a recommendation for any product.

The rest is here:

Conversational Computers: A Theme Accessible With This AI ETF - ETF Trends