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Research report on global Quantum Computing Technologies market 2020 with industry primary research, secondary research, product research, size, trends and Forecast.

The report presents a highly comprehensive and accurate research study on the globalQuantum Computing Technologies market. It offers PESTLE analysis, qualitative and quantitative analysis, Porters Five Forces analysis, and absolute dollar opportunity analysis to help players improve their business strategies. It also sheds light on critical Quantum Computing Technologies Marketdynamics such as trends and opportunities, drivers, restraints, and challenges to help market participants stay informed and cement a strong position in the industry. With competitive landscape analysis, the authors of the report have made a brilliant attempt to help readers understand important business tactics that leading companies use to maintainQuantum Computing Technologies market sustainability.

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

The regional analysis of Global Quantum Computing Technologies Market is considered for the key regions such as Asia Pacific, North America, Europe, Latin America and Rest of the World. North America is the leading/significant region across the world in terms of market share due to increasing usage of quantum computers by government agencies and aerospace & defense for machine learning in the region. Europe is estimated to grow at second largest region in the global Quantum Computing Technologies market over the upcoming years. Further, Asia-Pacific is anticipated to exhibit higher growth rate / CAGR over the forecast period 2019-2026 due to rising adoption of quantum computers by BFSI sectors in the region.

The major market player included in this report are:

D-Wave Systems Inc.

IBM Corporation

Lockheed Martin Corporation

Intel Corporation

Anyon Systems Inc.

Cambridge Quantum Computing Limited

The objective of the study is to define market sizes of different segments & countries in recent years and to forecast the values to the coming eight years. The report is designed to incorporate both qualitative and quantitative aspects of the industry within each of the regions and countries involved in the study. Furthermore, the report also caters the detailed information about the crucial aspects such as driving factors & challenges which will define the future growth of the market. Additionally, the report shall also incorporate available opportunities in micro markets for stakeholders to invest along with the detailed analysis of competitive landscape and product offerings of key players. The detailed segments and sub-segment of the market are explained below:

By Application:

Optimization

Machine Learning

Simulation

By Vertical:

BFSI

IT and Telecommunication

Healthcare

Transportation

Government

Aerospace & Defense

Others

By Regions:

North America

U.S.

Canada

Europe

UK

Germany

Asia Pacific

China

India

Japan

Latin America

Brazil

Mexico

Rest of the World

Furthermore, years considered for the study are as follows:

Historical year 2016, 2017

Base year 2018

Forecast period 2019 to 2026

Target Audience of the Global Quantum Computing Technologies Market in Market Study:

Key Consulting Companies & Advisors

Large, medium-sized, and small enterprises

Venture capitalists

Value-Added Resellers (VARs)

Third-party knowledge providers

Investment bankers

Investors

Have Any Query Or Specific Requirement?Ask Our Industry Experts!

Table of Contents:

Study Coverage:It includes study objectives, years considered for the research study, growth rate and Quantum Computing Technologies market size of type and application segments, key manufacturers covered, product scope, and highlights of segmental analysis.

Executive Summary:In this section, the report focuses on analysis of macroscopic indicators, market issues, drivers, and trends, competitive landscape, CAGR of the global Quantum Computing Technologies market, and global production. Under the global production chapter, the authors of the report have included market pricing and trends, global capacity, global production, and global revenue forecasts.

Quantum Computing Technologies Market Size by Manufacturer: Here, the report concentrates on revenue and production shares of manufacturers for all the years of the forecast period. It also focuses on price by manufacturer and expansion plans and mergers and acquisitions of companies.

Production by Region:It shows how the revenue and production in the global market are distributed among different regions. Each regional market is extensively studied here on the basis of import and export, key players, revenue, and production.

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Originally posted here:
Covid 19 Pandemic: Quantum Computing Technologies Market 2020, Share, Growth, Trends And Forecast To 2025 - 3rd Watch News

The TV series Devs took as its premise the idea that a quantum computer of sufficient power could simulate the world so completely that it could project events accurately back into the distant past (the Crucifixion or prehistory) and predict the future. At face value somewhat absurd, the scenario supplied a framework on which to hang questions about determinism and free will (and less happily, the Many Worlds interpretation of quantum mechanics).

Quite what quantum computers will do for molecular simulations remains to be seen, but the excitement about them shouldnt eclipse the staggering advances still being made in classical simulation. Full ab initio quantum-chemical calculations are very computationally expensive even with the inevitable approximations they entail, so it has been challenging to bring this degree of precision to traditional molecular dynamics, where molecular interactions are still typically described by classical potentials. Even simulating pure water, where accurate modelling of hydrogen bonding and the ionic disassociation of molecules involves quantum effects, has been tough.

Now a team that includes Linfeng Zhang and Roberto Car of Princeton University, US, has conducted ab initio molecular dynamics simulations for up to 100 million atoms, probing timescales up to a few nanoseconds.1 Sure, its a long way from the Devs fantasy of an exact replica of reality. But it suggests that simulations with quantum precision are reaching the stage where we can talk not in terms of handfuls of molecules but of bulk matter.

How do they do it? The trick, which researchers have been exploring for several years now, is to replace quantum-chemical calculations with machine learning (ML). The general strategy of ML is that an algorithm learns to solve a complex problem by being trained with many examples for which the answers are already known, from which it deduces the general shape of solutions in some high-dimensional space. It then uses that shape to interpolate for examples that it hasnt seen before. The familiar example is image interpretation: the ML system works out what to look for in photos of cats, so that it can then spot which new images have cats in them. It can work remarkably well so long as it is not presented with cases that lie far outside the bounds of the training set.

The approach is being widely used in molecular and materials science, for example to predict crystal structures from elemental composition,2-3 or electronic structure from crystal structure.4-5 In the latter case, bulk electronic properties such as band gaps have traditionally been calculated using density functional theory (DFT), an approximate way to solve the quantum-mechanical equations of many-body systems. Here the spatial distribution of electron density is computationally iterated from some initial guess until it fits the equations in a self-consistent way. But its computationally intensive, and ML circumvents the calculations by figuring out from known cases what kind of electron distribution a given configuration of atoms will have.

The approach can in principle be used for molecular dynamics by recalculating the electron densities at each time step. Zhang and colleagues have now shown how far this idea can be pushed using supercomputing technology, clever algorithms, and state-of-the-art artificial intelligence.6 They present results for simulations of up to 113 million atoms for the test case of a block of copper atoms, enabling something approaching a prediction of bulk-like mechanical behaviour from quantum chemistry. Their simulations of liquid water, meanwhile, contain up to 12.6 million atoms.

For small systems where the comparison to full quantum DFT calculations can be made, the researchers find electron distributions essentially indistinguishable from the full calculations, while gaining 45 orders of magnitude in speed. Their system can capture the full phase diagram of water over a wide range of temperature and pressure, and can simulate processes such as ice nucleation. In some situations water can be coarse-grained such that hydrogen bonding can still be modelled without including the hydrogen atoms explicitly.7 The researchers say it should be possible soon to follow such processes on timescales approaching microseconds for about a million water molecules, enabling them to look at processes such as droplet and ice formation in the atmosphere.

For small systems where the comparison to full quantum DFT calculations can be made, the researchers find electron distributions essentially indistinguishable from the full calculations, while gaining 45 orders of magnitude in speed. Their system can capture the full phase diagram of water over a wide range of temperature and pressure, and can simulate processes such as ice nucleation. The researchers say it should be possible soon to follow such processes on timescales approaching microseconds for about a million water molecules, enabling them to look at processes such as droplet and ice formation in the atmosphere.

Both of these test cases are helped by being relatively homogeneous, involving largely identical atoms or molecules. Still, the prospects of this deep-learning approach look good for studying much more heterogeneous systems such as complex alloys.8 One very attractive goal is, of course, biomolecular systems, where the ability to model fully solvated proteins, membranes and other cell components could help us understand complex mesoscale cell processes and predict the behaviour of drug candidates. One challenge here is how to include long-range interactions such as electrostatic forces.

Its a long way from Devs-style simulations of minds and histories, which will perhaps only ever be fantasies. But one scene in that series showed what might be a more tractable goal: the simulation of a growing snowflake. What a wonderful way that would be to advertise the simulators art.

1. Jia et al., arXiv, 2020 http://www.arxiv.org/abs/2005.00223 (submitted, ACM, New York, 2020)

2 C C Fischer et al, Nat. Mater., 2006, 5, 641 (DOI:10.1038/nmat1691)

3 N Mounet et al, Nat. Nanotechnol., 2018, 13, 246 (DOI: 10.1038/s41565-017-0035-5)

4 Y Dong et al, npj Comput. Mater., 2019, 5, 26 (DOI:10.1038/s41524-019-0165-4)

5 A Chandrasekaran et al, npj Comput. Mater., 2019, 5, 22 (DOI:10.1038/s41524-019-0162-7)

6 L Zhang et al, Phys. Rev. Lett., 2018, 120, 143001 (DOI:10.1103/PhysRevLett.120.143001)

7. L Zhang et al, J. Chem. Phys., 2018, 149, 034101 (DOI:10.1063/1.5027645)

8. F-Z Dai et al., J. Mater. Sci. Technol., 2020, 43, 168 (DOI:10.1016/j.jmst.2020.01.005)

See original here:
Molecular dynamics used to simulate 100 million atoms | Opinion - Chemistry World

As AOL celebrates its 35th birthday on May 24, we're looking back at the internet pioneer's legacy, including its most influential figures and noteworthyhistory.

Aside from the endless pop culture references and memorableInstant Messenger culture, there is one figure that stands out as the brand's most recognizable. And no, we're not talking about Steve Case, who co-founded the company, then named"Quantum Computer Services," back in 1985... or the yellow AIM icon.

In fact, even ifyou weren't one of America Online's legacy users, you're definitely familiar with the "You've Got Mail" greeting.Elwood Edwards, now in his mid-60s, was thevoice behind the iconic welcome, as well as three other of the software'ssignature catchphrases:"Hello," "Goodbye" and "File's Done."

In 1989, Edwardswas working as a voiceover actor on television commercials when he recorded America Online's voice for only $200. His former wife, Karen, was an employee atQuantum Computer Services at the timeand had heard the company was looking for a voice actor.

"[She] overheard [future CEO] Steve Case talking about adding a voice to the then-upcoming AOL software in 1989," Edwards told AOL in 2012. "So, she volunteered my voice and on a cassette deck in my living room, I recorded the phrases that you've come to know."

Most recently, Edwards opened up about the legacy of his voice in the podcastTwenty Thousand Hertz.

"I've been an announcer my entire broadcasting career," Edwards said in the podcast's Sept. 2019 episode."It was nothing new to me to hear my voice coming out of a little speaker. I didn't really think anything of it at the time."

He continued:

"I don't think anyone had any idea what it would become. Certainly, had I realized it at the time I would now be retired, but I'm not. Even today, I have an AOL account, an email account...When you sign on to that, you still hear me say, 'You've Got Mail.'"

Edwards retired in 2014 after 47 years in television, but AOL users and Uber passengers are still quick to recognize his classic voice. In 2016, Edwards wasdriving for the rideshare platform when a passenger made the connection.

And although he doesn't necessarily go around "blowing his horn" about his identity, Edwards occasionally reminds people of his fame while standing behind their computers and saying the phrase.

Even three decades later, AOL email users can still hear that iconic three word phrase, voiced by Edwards, whenthey open their inboxes. All they have to do is turn it on in their settings.

"What started off as a test has continued to this day," he proudly shared.

More from Aol.com: Drag queen stuns with paper-folding math lesson: 'Better than our school systems' 'Extensive search' for missing soldier after keys, wallet found Teacher stunned by response to quarantine video: 'I cant believe how well received it has been'

More:
Meet the Uber driver behind AOL's 'You've Got Mail' greeting - AOL

Microsoft today announced that Azure Quantum, its partner-centric quantum computing platform for developers who want to get started with quantum computing, is now in limited preview. First announced at Microsoft Ignite 2019, Azure Quantum brings together the hardware from IonQ, Honeywell, QCI and Microsoft, services from the likes of 1QBit, and the classical computing capabilities of the Azure cloud. With this move to being in limited preview, Microsoft is now opening the service up to a small number of select partners and customers.

At its current stage, quantum computing isnt exactly a mission-critical capability for any business, but given how fast things are moving and how powerful the technology will be once its matured a bit over the next few years, many experts argue that now is the time to get started especially because of how different quantum computing is from classical computing and how it will take developers a while to develop.

At Ignite, Microsoft also open-sourced its Quantum Development Kit, compilers and simulators.

With all of this, the company is taking a different approach from some of its competitors. In addition, Microsoft also currently has to partner with quantum hardware companies simply because its own quantum hardware efforts havent quite reached the point where they are viable. The company is taking a very different approach from the likes of IBM or Rigetti by betting on a different kind of qubit at the core of its machine. And while it has made some breakthroughs in recent months, it doesnt yet have a working qubit or if it does, it hasnt publicly talked about it.

See the rest here:
Microsofts quantum computing platform is now in limited preview - TechCrunch

The coronavirus is proving that we have to move faster in identifying and mitigating epidemics before they become pandemics because, in todays global world, viruses spread much faster, further, and more frequently than ever before.

If COVID-19 has taught us anything, its that while our ability to identify and treat pandemics has improved greatly since the outbreak of the Spanish Flu in 1918, there is still a lot of room for improvement. Over the past few decades, weve taken huge strides to improve quick detection capabilities. It took a mere 12 days to map the outer spike protein of the COVID-19 virus using new techniques. In the 1980s, a similar structural analysis for HIV took four years.

But developing a cure or vaccine still takes a long time and involves such high costs that big pharma doesnt always have incentive to try.

Drug discovery entrepreneur Prof. Noor Shaker posited that Whenever a disease is identified, a new journey into the chemical space starts seeking a medicine that could become useful in contending diseases. The journey takes approximately 15 years and costs $2.6 billion, and starts with a process to filter millions of molecules to identify the promising hundreds with high potential to become medicines. Around 99% of selected leads fail later in the process due to inaccurate prediction of behavior and the limited pool from which they were sampled.

Prof. Shaker highlights one of the main problems with our current drug discovery process: The development of pharmaceuticals is highly empirical. Molecules are made and then tested, without being able to accurately predict performance beforehand. The testing process itself is long, tedious, cumbersome, and may not predict future complications that will surface only when the molecule is deployed at scale, further eroding the cost/benefit ratio of the field. And while AI/ML tools are already being developed and implemented to optimize certain processes, theres a limit to their efficiency at key tasks in the process.

Ideally, a great way to cut down the time and cost would be to transfer the discovery and testing from the expensive and time-inefficient laboratory process (in-vitro) we utilize today, to computer simulations (in-silico). Databases of molecules are already available to us today. If we had infinite computing power we could simply scan these databases and calculate whether each molecule could serve as a cure or vaccine to the COVID-19 virus. We would simply input our factors into the simulation and screen the chemical space for a solution to our problem.

In principle, this is possible. After all, chemical structures can be measured, and the laws of physics governing chemistry are well known. However, as the great British physicist Paul Dirac observed: The underlying physical laws necessary for the mathematical theory of a large part of physics and the whole of chemistry are thus completely known, and the difficulty is only that the exact application of these laws leads to equations much too complicated to be soluble.

In other words, we simply dont have the computing power to solve the equations, and if we stick to classical computers we never will.

This is a bit of a simplification, but the fundamental problem of chemistry is to figure out where electrons sit inside a molecule and calculate the total energy of such a configuration. With this data, one could calculate the properties of a molecule and predict its behavior. Accurate calculations of these properties will allow the screening of molecular databases for compounds that exhibit particular functions, such as a drug molecule that is able to attach to the coronavirus spike and attack it. Essentially, if we could use a computer to accurately calculate the properties of a molecule and predict its behavior in a given situation, it would speed up the process of identifying a cure and improve its efficiency.

Why are quantum computers much better than classical computers at simulating molecules?

Electrons spread out over the molecule in a strongly correlated fashion, and the characteristics of each electron depend greatly on those of its neighbors. These quantum correlations (or entanglement) are at the heart of the quantum theory and make simulating electrons with a classical computer very tricky.

The electrons of the COVID-19 virus, for example, must be treated in general as being part of a single entity having many degrees of freedom, and the description of this ensemble cannot be divided into the sum of its individual, distinguishable electrons. The electrons, due to their strong correlations, have lost their individuality and must be treated as a whole. So to solve the equations, you need to take into account all of the electrons simultaneously. Although classical computers can in principle simulate such molecules, every multi-electron configuration must be stored in memory separately.

Lets say you have a molecule with only 10 electrons (forget the rest of the atom for now), and each electron can be in two different positions within the molecule. Essentially, you have 2^10=1024 different configurations to keep track of rather just 10 electrons which would have been the case if the electrons were individual, distinguishable entities. Youd need 1024 classical bits to store the state of this molecule. Quantum computers, on the other hand, have quantum bits (qubits), which can be made to strongly correlate with one another in the same way electrons within molecules do. So in principle, you would need only about 10 such qubits to represent the strongly correlated electrons in this model system.

The exponentially large parameter space of electron configurations in molecules is exactly the space qubits naturally occupy. Thus, qubits are much more adapted to the simulation of quantum phenomena. This scaling difference between classical and quantum computation gets very big very quickly. For instance, simulating penicillin, a molecule with 41 atoms (and many more electrons) will require 10^86 classical bits, or more bits than the number of atoms in the universe. With a quantum computer, you would only need about 286 qubits. This is still far more qubits than we have today, but certainly a more reasonable and achievable number.The COVID-19 virus outer spike protein, for comparison, contains many thousands of atoms and is thus completely intractable for classical computation. The size of proteins makes them intractable to classical simulation with any degree of accuracy even on todays most powerful supercomputers. Chemists and pharma companies do simulate molecules with supercomputers (albeit not as large as the proteins), but they must resort to making very rough molecule models that dont capture the details a full simulation would, leading to large errors in estimation.

It might take several decades until a sufficiently large quantum computer capable of simulating molecules as large as proteins will emerge. But when such a computer is available, it will mean a complete revolution in the way the pharma and the chemical industries operate.

The holy grail end-to-end in-silico drug discovery involves evaluating and breaking down the entire chemical structures of the virus and the cure.

The continued development of quantum computers, if successful, will allow for end-to-end in-silico drug discovery and the discovery of procedures to fabricate the drug. Several decades from now, with the right technology in place, we could move the entire process into a computer simulation, allowing us to reach results with amazing speed. Computer simulations could eliminate 99.9% of false leads in a fraction of the time it now takes with in-vitro methods. With the appearance of a new epidemic, scientists could identify and develop a potential vaccine/drug in a matter of days.

The bottleneck for drug development would then move from drug discovery to the human testing phases including toxicity and other safety tests. Eventually, even these last stage tests could potentially be expedited with the help of a large scale quantum computer, but that would require an even greater level of quantum computing than described here. Tests at this level would require a quantum computer with enough power to contain a simulation of the human body (or part thereof) that will screen candidate compounds and simulate their impact on the human body.

Achieving all of these dreams will demand a continuous investment into the development of quantum computing as a technology. As Prof. Shohini Ghose said in her 2018 Ted Talk: You cannot build a light bulb by building better and better candles. A light bulb is a different technology based on a deeper scientific understanding. Todays computers are marvels of modern technology and will continue to improve as we move forward. However, we will not be able to solve this task with a more powerful classical computer. It requires new technology, more suited for the task.

(Special thanks Dr. Ilan Richter, MD MPH for assuring the accuracy of the medical details in this article.)

Ramon Szmuk is a Quantum Hardware Engineer at Quantum Machines.

Read more here:
Quantum computing will (eventually) help us discover vaccines in days - VentureBeat