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New Jersey, United States,- A detailed research study on Quantum Computing Market recently published by Verified Market Research. This is the latest report, which covers the time COVID-19 impact on the market. Pandemic Coronavirus (COVID-19) has affected every aspect of global life. This has brought some changes in market conditions. Rapidly changing market scenario and the initial assessment and the future of this effect is included in the report. Reports put together a brief analysis of the factors affecting the growth of the current business scenarios in various areas. Important information relating to the size of the industry analysis, sharing, application, and statistics summed up in the report to present the ensemble prediction. In addition, this report includes an accurate competitive analysis of major market players and their strategies during the projection period.

This report includes market size estimates for the value (million USD) and volume (K Units). Both top-down and bottom-up approach has been used to estimate the size of the market and validate the Market of Quantum Computing, to estimate the size of the various submarkets more dependent on the overall market. Key players in the market have been identified through secondary research and their market share has been determined through primary and secondary research. All the shares percentage, split, and the damage have been determined using secondary sources and primary sources verified.

Leading Quantum Computing manufacturers/companies operating at both regional and global levels:

Quantum Computing Market Competitive Landscape & Company Profiles

Competitor analysis is one of the best sections of the report that compares the progress of leading players based on crucial parameters, including market share, new developments, global reach, local competition, price, and production. From the nature of competition to future changes in the vendor landscape, the report provides in-depth analysis of the competition in the Quantum Computing market.

Segmental Analysis

Both developed and emerging regions are deeply studied by the authors of the report. The regional analysis section of the report offers a comprehensive analysis of the global Quantum Computing market on the basis of region. Each region is exhaustively researched about so that players can use the analysis to tap into unexplored markets and plan powerful strategies to gain a foothold in lucrative markets.

Regions Covered in these Report:

Asia Pacific (China, Japan, India, and Rest of Asia Pacific)Europe (Germany, the UK, France, and Rest of Europe)North America (the US, Mexico, and Canada)Latin America (Brazil and Rest of Latin America)Middle East & Africa (GCC Countries and Rest of Middle East & Africa)

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Quantum Computing Market Research Methodology

The research methodology adopted for the analysis of the market involves the consolidation of various research considerations such as subject matter expert advice, primary and secondary research. Primary research involves the extraction of information through various aspects such as numerous telephonic interviews, industry experts, questionnaires and in some cases face-to-face interactions. Primary interviews are usually carried out on a continuous basis with industry experts in order to acquire a topical understanding of the market as well as to be able to substantiate the existing analysis of the data.

Subject matter expertise involves the validation of the key research findings that were attained from primary and secondary research. The subject matter experts that are consulted have extensive experience in the market research industry and the specific requirements of the clients are reviewed by the experts to check for completion of the market study. Secondary research used for the Quantum Computing market report includes sources such as press releases, company annual reports, and research papers that are related to the industry. Other sources can include government websites, industry magazines and associations for gathering more meticulous data. These multiple channels of research help to find as well as substantiate research findings.

Table of Content

1 Introduction of Quantum Computing Market

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

2 Executive Summary

3 Research Methodology of Verified Market Research

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

4 Quantum Computing Market Outlook

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

5 Quantum Computing Market, By Deployment Model

5.1 Overview

6 Quantum Computing Market, By Solution

6.1 Overview

7 Quantum Computing Market, By Vertical

7.1 Overview

8 Quantum Computing Market, By Geography

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

9 Quantum Computing Market Competitive Landscape

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

10 Company Profiles

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

11 Appendix

11.1 Related Research

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Read the original post:
Quantum Computing Market Analysis, Trends, Top Manufacturers, Growth, Statistics, Opportunities and Forecast To 2026 - Cole of Duty

China's President Xi Jinping is shown around the offices of Huawei Technologies by Huawei's Ren ... [+] Zhengfei in London on October 21, 2015.

What if the U.S. had the economic equivalent of a Sputnik moment and the White House failed to notice?

This, unfortunately, is now more a rhetorical question than an active one.

Admittedly, its become a clich to compare this or that shock to how the Soviet Unions 1957 satellite launch rocked America. But economist Richard Duncan has a point that Beijings aggressive investments in 5G and other technologies, with President Donald Trump focused on bringing back coal, is its own national emergency.

There is no mystery as to why China will soon overtake the U.S. as the worlds leading technological, economic and military superpower if current trends continue, says Duncan, author of The New Depression: The Breakdown Of The Paper Money Economy. Ultimately, he says, theres only one way for the U.S. to retain its global preeminence: it must invest more than China.

How much? Duncan reckons about $8 trillion over 10 years. Top investment targets are artificial intelligence, biotech, genetic engineering, green energy, nanotechnology, neural sciences, quantum computing and robotics.

This, of course, is an unthinkable amount of money. But since January 2017, President Trump has made it easy for China to Sputnik the globes biggest economy.

The popular narrative is that Trump, by taking on Beijing like no other U.S. leader before, is leveling the playing field and scoring wins for American workers. That mightve been true in 1985, when blunt instruments like tariffs turned economic tides. In 2020, an old-school trade war only works if youre simultaneously building economic muscle. For all his wannabe strongman theatrics, Trumps complacency is weakening Americas endurance while China raises its game.

China has daunting challenges. Not least of them is slowing growth, runaway debt and a geopolitical confrontation of choice over Hong Kong. Yet Beijing is multitasking in ways Washington isnt. Xi is propping up this years growth, while also investing in this decades strategy to become No. 1.

Trumps 1985 mindset seems to be missing this phenomenon. Nowadays, President Xi Jinpings government barely mentions the Made in China 2025 that irked Trumpworld so much.

Xi is clearly happy to let Trump surrogates Peter Navarro and Larry Kudlow think tariffs scared Beijing into shelving plans to dominate tech over the next five to 10 years. Hardly. The endeavor is now referred to as new infrastructure.

Yet theres zero new or innovative about how Trumps team is approaching economic retooling. And in that sense, Xis team may actually welcome another Trump term. It wouldnt be fun in the short run, but it would enable Xi to position China as a stabler, more cooperative power than Trumps America.

In the 80s, tax cuts may have catalyzed investments in research and development and audacious risk-taking. In the Trump era, theyre little more than fuel for dividends and share buybacks that do little to hone American ingenuity and competitiveness.

U.S. President Donald Trump is flanked by administration officials while speaking about U.S. ... [+] relations with China in the Rose Garden at the White House May 29, 2020 in Washington, D.C.

To be sure, coronavirus fallout takes precedence over building a more dynamic and productive economy. Yet the biggest plan to do that isnt coming from Trumps Republicans but the Democrats. Case in point: Senate Minority Leader Charles Schumers plan, unveiled in November, to fund $100 billion of investment in AI and other cutting-edge sectors.

At the moment, the U.S. has been earmarking such non-defense-related financing at about $1 billion annually. If you want to know why Xis government is so confident about Chinas trajectory, this lack of scale is as good a place as any to start. Even if Trumps Republicans were getting behind Schumers proposal (theyre not), its not enough.

If the U.S. invested an additional $100 billion in R&D over five years$20 billion a year starting in 2021China would still retain its lead in R&D investment, Duncan says. The U.S. will have to invest much more than $100 billion in R&D if it is going to maintain its lead over China.Fortunately, it can easily afford to do so.

This, too, might seem a stretch given the trillions of dollars Washington is already spending on Covid-19 rescue packages. But U.S. borrowing costs may never be this low again, as Nobel laureate Paul Krugman has been arguing.

The Trump-era Republican Party, though, is keener on wrestling jobs and wealth from China than generating new innovative energy. This strategy explains why Trumps presidency is actually making China great again.

No, Xis government isnt enjoying taxes on $500 billion-plus of goods China sends to the U.S. Trump targeting the Huaweis of the world, blacklisting dozens of other mainland companies and making it harder to list on New York exchanges. Beijing isnt happy to be among Trumps favorite Twitter bugbears or to be blamed for his dreadful handling of the coronavirus.

But Trumps approach to Covid-19 mirrors his policies toward China. In both cases, Trump has treated the symptoms, not the underlying problems. In both, hes favored spin, deception and projection over genuine solutions. In both, things are sure to end badly for Americas economic standing five years from now.

The U.S., notes Dan Wang of Gavekal Research, is broadening efforts to constrain Chinese tech firms, using sanctions that impact suppliers to Chinas government, the military and other sectors. What, however, is Trumps White House doing to rekindle American innovation? Its akin to trying to win a race by flattening your opponents tires. You may triumph today, but the other car will still be faster tomorrow.

Given the stakes, Duncans $800 billion per year until 2031 is less a choice than a necessary expenditure to keep up with China. Dont think of this $8 trillion as a bill to pay. Think of it as the only thing standing between the U.S. and No. 2 status.

Here is the original post:
China Is $8 Trillion Away From Eclipsing Trumps Economy - Forbes

A joint research team from the University of Hong Kong (HKU), Institute of Physics at Chinese Academy of Science, Songshan Lake Materials Laboratory, Beihang University in Beijing and Fudan University in Shanghai, has provided a successful example of modern era quantum material research. By means of the state-of-art quantum many-body simulations, performed on the worlds fastest supercomputers (Tianhe-I and Tianhe-III protype at National Supercomputer Center in Tianjin and Tianhe-II at National Supercomputer Center in Guangzhou), they achieved accurate model calculations for a rare-earth magnet TmMgGaO4 (TMGO). They found that the material, under the correct temperature regime, could realise the the long-sought-after two-dimensional topological Kosterlitz-Thouless (KT) phase, which completed the pursuit of identifying the KT physics in quantum magnetic materials for half a century. The research work has been published in Nature Communications.

Quantum materials are becoming the cornerstone of the continuous prosperity of human society. From the next-generation AI computing chips that go beyond Moores law (the law is the observation that the number of transistors in a dense integrated circuit doubles about every two years, our PCs and smartphones are all based on the success of it. Nevertheless, as the size of the transistors are becoming smaller to the scale of nanometer, the behaviour of electrons are subject to quantum mechanics, Moores law is expected to breakdown very soon), to the high speed Maglev train and the topological unit for quantum computers, investigations along these directions all belong to the arena of quantum material research.

However, such research is by no means easy. The difficulty lies in the fact that scientists have to solve the millions of thousands of the electrons in the material in a quantum mechanical way (hence quantum materials are also called quantum many-body systems), this is far beyond the time of paper and pencil, and requires instead modern quantum many-body computational techniques and advanced analysis. Thanks to the fast development of the supercomputing platforms all over the world, scientists and engineers are now making great use of these computation facilities and advanced mathematical tools to discover better materials to benefit our society.

The research is inspired by the KT phase theory avocated by J Michael Kosterlitz, David J Thouless and F Duncan M Haldane, laureates of the Nobel Prize in Phyiscs 2016. They were awarded for their theoretical discoveries of topological phase and phase transitions of matter. Topology is a new way of classifying and predicting the properties of materials in condensed matter physics, and is now becoming the main stream of quantum material research and industry, with broad potential applications in quantum computing, lossless transmission of signals for information technology, etc. Back in the 1970s, Kosterlitz and Thouless had predicted the existence of topological phase, hence named after them as the KT phase, in quantum magnetic materials. However, although such phenomena have been found in superfluids and superconductors, KT phase has yet been realised in bulk magnetic material.

The joint team is led by Dr Zi Yang Meng from HKU, Dr Wei Li from Beihang Univeristy and Professor Yang Qi from Fudan University. Their joint effort has revealed the comprehensive properties of the material TMGO. For example, in Figure 2, by self-adjustable tensor network calculation, they computed the properties of the model system at different temperatures, magnetic field, and by comparing with the corresponding experimental results of the material, they identified the correct microscopic model parameters. With the correct microscopic model on hand, they then performed quantum Monte Carlo simulation and obtained the neutron scattering magnetic spectra at different temperatures (neutron scattering is the established detection method for material structure and their magnetic properties, the closest such facility to Hong Kong is the China Spallation Neutron Source in Dongguan, Guangdong). As shown in Figure 3, the magnetic spectra with its unique signature at the M point is the dynamical fingerprint of the topological KT phase that has been proposed more than half-a-century ago.

This research work provides the missing piece of topological KT phenomena in the bulk magnetic materials, and has completed the half-a-century pursuit which eventually leads to the Nobel Physics Prize of 2016. Since the topological phase of matter is the main theme of condensed matter and quantum material research nowadays, it is expected that this work will inspire many follow-up theoretical and experimental researches, and in fact, promising results for further identification of the topological properties in quantum magnet have been obtained among the joint team and our collaborators, said Dr Meng.

Dr Meng added: The joint team research across Hong Kong, Beijing and Shanghai also sets up the protocol of modern quantum material research, such protocol will certainly lead to more profound and impactful discoveries in quantum materials. The computation power of our smartphone nowadays is more powerful than the supercomputers 20 years ago, one can optimistically foresee that with the correct quantum material as the building block, personal devices in 20 years time can certainly be more powerful than the fastest supercomputers right now, with minimal energy cost of everyday battery.

Read more here:
Quantum material research connecting physicists in Hong Kong, Beijing and Shanghai facilitates discovery of better materials that benefit our society...

Archer staff operating the specialised conduction atomic force microscopy instrumentation required to perform the measurements.

Archer Materials has announced a milestone in its race to build a room-temperature quantum computing quantum bit (qubit) processor, revealing it has successfully performed its first measurement on a single qubit component.

"We have successfully performed our first measurement on a single qubit component, which is the most important component, marking a significant period moving forward in the development of Archer's 12CQ quantum computing chip technology," CEO Dr Mohammad Choucair said.

"Building and operating the 12CQ chip requires measurements to be successfully performed at the very limits of what can be achieved technologically in the world today."

See also:Australia's ambitious plan to win the quantum race

Choucair said directly proving room-temperature conductivity of the 12CQ chip qubit component advances Archer's development towards a working chip prototype.

Archer said conductivity measurements on single qubit components were carried out using conductive atomic force microscopy that was configured using "state-of-the-art instrumentation systems", housed in a semiconductor prototype foundry cleanroom.

"The measurements directly and unambiguously proved, with nanometre-scale precision, the conductivity of single qubits at room-temperature in ambient environmental conditions (e.g. in the presence of air, moisture, and at normal atmospheric pressures," Archer said in a statement.

It said the measurements progress its technological development towards controlling quantum information that reside on individual qubits, which is a key componentry requirement for a working quantum computing qubit processor.

Another key component is readout.

"Control must be performed prior to readout, as these subsequent steps represent a logical series in the 12CQ quantum computing chip function," Archer wrote.

See also: What is quantum computing? Understanding the how, why and when of quantum computers

In announcing last week it was progressing work on its graphene-based biosensor technology, Archer said it was focusing on establishing commercial partnerships to bring its work out of the lab and convert it into viable products.

Archer on Monday said it intends to develop the 12CQ chip to be sold directlyand have the intellectual property rights to the chip technology licensed.

"The technological significance of the work is inherently tied to the commercial viability of the 12CQ technology. The room-temperature conductivity potentially enables direct access to the quantum information stored in the qubits by means of electrical current signals on-board portable devices, which require conducting materials to operate, for both control and readout," Choucair added.

He said the intrinsic materials feature of conductivity in Archer's qubit material down to the single qubit level represents a "significant commercial advantage" over competing qubit proposals that rely on insulating materials, such as diamond-based materials or photonic qubit architectures.

More here:
Archer touts performing early-stage validation of quantum computing chip - ZDNet

In March, I explored the enterprise readiness of quantum computing in Quantum computing is right around the corner, but cooling is a problem. What are the options? I also detailed potential industry use cases, from supply chain to banking and finance. But what are the industry giants pursuing?

Recently, I listened to two somewhat different perspectives on quantum computing. One is Googles (public) ten-year plan.

Google plans to search for commercially viable applications in the short term, but they dont think there will be many for another ten years - a time frame I've heard one referred to as bound but loose. What that meant was, no more than ten, maybe sooner. In the industry, the term for the current state of the art is NISQ Noisy, Interim Scale Quantum Computing.

The largest quantum computers are in the 50-70 qubit range, and Google feels NISQ has a ceiling of maybe two hundred. The "noisy" part of NISQ is because the qubits need to interact and be nearby. That generates noise. The more qubits, the more noise, and the more challenging it is to control the noise.

But Google suggests the real unsolved problems in fields like optimization, materials science, chemistry, drug discovery, finance, and electronics will take machines with thousands of qubits and even envision one million on a planar array etched in aluminum. Major problems need solving such noise elimination, coherence, and lifetime (a qubit holds its position in a tiny time slice).

In the meantime, Google is seeking customers to work with them to find applications working with Google researchers. Quantum computing needs algorithms as much as it needs qubits. It requires customers with a strong in-house science team and a commitment of three years. Whatever is discovered will be published as open source.

In summary, Google does not see commercial value in NISQ. They are using NISQ to discover what quantum computing can do that has any commercial capability.

First of all, if you have a picture in your mind of a quantum computer, chances are you are not including an essential element a conventional computer. According toQuantum Computing, Progress, and Prospects:

Although reports in the popular press tend to focus on the development of qubits and the number of qubits in the current prototypical quantum computing chip, any quantum computer requires an integrated hardware approach using significant conventional hardware to enable qubits to be controlled, programmed, and read out.

The author is undoubtedly correct. Most material about quantum computers never mentions this, and it raises quite a few issues that can potentially dilute the gee-whiz aspect. I'd heard this first from Itamar Sivan, Ph.D., CEO, Quantum Machines. He followed with the quip that technically, quantum computers aren't computers. Its that simple. They are not Turing Machines. File this under the category of "You're Not Too Old to Learn Something New.

From (Hindi) Theory of Computation - Turing Machine:

A Turing machine is a mathematical model of computation that defines an abstract machine, which manipulates symbols on a strip of tape according to a table of rules. Despite the model's simplicity, given any computer algorithm, a Turing machine capable of simulating that algorithm's logic can be constructed.

Dr. Sivan clarified this as follows:

Any computer to ever be used, from the early-days computers, to massive HPCs, are all Turing-machines, and are thereforeequivalent to one another. All computers developedand manufactured in the last decades, are all merelybigger and more compact variations of one another. A quantum computer however is not MERELY a more advanced Turing machine, it is a different type of machine, and classical Turing machines are not equivalent to quantum computers as they are equivalent to one another.

Therefore, the complexity of running particular algorithms on quantum computers is different from the complexity of running them on classical machines. Just to make it clear, a quantum computer can be degenerated to behave like a classical computer, but NOT vice-versa.

There is a lot more to this concept, but most computers you've ever seen or heard of are Turing Machines, except Quantum computers. This should come as no surprise because anything about quantum mechanics is weird and counter-intuitive, so why would a quantum computer be any different?

According to Sivan, a quantum computer needs three elements to perform: a quantum computer and an orchestration platform of (conventional) hardware and software. There is no software in a quantum computer. The platform manages the progress of their algorithm through, mostly laser beams pulses. The logic needed to operate the quantum computer resides with and is controlled by the orchestration platform.

The crucial difference in Google's and Quantum Machines' strategy is that Google views the current NISQ state of affairs as a testbed for finding algorithms and applications for future development. At the same time, Sivan and his company produced an orchestration platform to put the current technology in play. Their platform is quantum computer agnostic it can operate with any of them. Sivan feels that focusing solely on the number of qubits is just part of the equation. According to Dr. Sivan:

While today's most advanced quantum computers only have a relatively small number of available qubits (53 for IBM's latest generation and 54 for Google's Sycamore processor), we cannot maximize the potential of even this relatively small count. We are leaving a lot on the table with regards to what we can already accomplish with the computing power we already have. While we should continue to scale up the number of qubits, we also need to focus on maximizing what we already have.

Ive asked a few quantum computer scientists if quantum computers can solve the Halting Problem.In Wikipedia:

The halting problem is determining, from a description of an arbitrarycomputer programand an input, whether the program will finish running, or continue to run forever.Alan Turingproved in 1936 that a generalalgorithmto solve the halting problem for all possible program-input pairs could not exist.

That puts it in a class of problems that are undecidable. Oddly, opinion was split onthequestion, despite Turings Proof. Like Simplico said to Galileo inDialogues Concerning Two New Sciences, If Aristotle had not said otherwise I would have believed it.

There are so many undecidable problems in math that I wondered if some of these might fall out.For example, straight from current AI problems, Planning in aPartially observable Markov decision process is considered undecidable. A million qubits? Maybe not. After all, Dr. Sivan pointed out that toreplicate in a classical processor, the information in just a 300 qubit quantum processor would require more transistors than all of the atoms inthe universe.

I've always believed that action speaks louder than words. While Google is taking the long view, Quantum Machines provides the platform to see how far we can go with current technology. Googles tactics are familiar. Every time you use TensorFlow, it gets better. Every time play with their autonomous car, it gets better. Their collaboration with a dozen or so technically advanced companies makes their quantum technology better.

Originally posted here:
The technical realities of functional quantum computers - is Googles ten-year plan for Quantum Computing viable? - Diginomica