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Moreover, the Topological Quantum Computing report offers a detailed analysis of the competitive landscape in terms of regions and the major service providers are also highlighted along with attributes of the market overview, business strategies, financials, developments pertaining as well as the product portfolio of the Topological Quantum Computing market. Likewise, this report comprises significant data about market segmentation on the basis of type, application, and regional landscape. The Topological Quantum Computing market report also provides a brief analysis of the market opportunities and challenges faced by the leading service provides. This report is specially designed to know accurate market insights and market status.

By Regions:

* North America (The US, Canada, and Mexico)

* Europe (Germany, France, the UK, and Rest of the World)

* Asia Pacific (China, Japan, India, and Rest of Asia Pacific)

* Latin America (Brazil and Rest of Latin America.)

* Middle East & Africa (Saudi Arabia, the UAE, , South Africa, and Rest of Middle East & Africa)

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Table of Content

1 Introduction of Topological Quantum Computing Market

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

2 Executive Summary

3 Research Methodology

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

4 Topological 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 Topological Quantum Computing Market, By Deployment Model

5.1 Overview

6 Topological Quantum Computing Market, By Solution

6.1 Overview

7 Topological Quantum Computing Market, By Vertical

7.1 Overview

8 Topological 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 Topological 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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Topological Quantum Computing Market Growth by Top Companies, Trends by Types and Application, Forecast to 2026 - Cole of Duty

Written by AZoQuantumMay 13 2020

The amount of energy needed to make or disintegrate a molecule can now be calculated more accurately than traditional methods using a new machine learning tool. Although the new tool can only deal with simple molecules at present, it opens the door to gain future insights into quantum chemistry.

Using machine learning to solve the fundamental equations governing quantum chemistry has been an open problem for several years, and theres a lot of excitement around it right now.

Giuseppe Carleo, Research Scientist, Center for Computational Quantum Physics, Flatiron Institute

Carleo, who is the co-creator of the tool, added that better insights into the formation and degradation of molecules could expose the inner workings of the chemical reactions crucial to life.

Carleo and his colleagues Kenny Choo from the University of Zurich and Antonio Mezzacapo from the IBM Thomas J. Watson Research Center in Yorktown Heights, New York, published their study in Nature Communications on May 12th, 2020.

The tool developed by the researchers predicts the energy required to put together or break apart a molecule, for example, ammonia or water. For this calculation, it is necessary to determine the electronic structure of the molecule, which comprises the collective behavior of the electrons binding the molecule together.

The electronic structure of a molecule is complex to find and requires determining all the possible states the electrons in the molecule could be in, along with the probability of each state.

Electrons interact and entangle quantum mechanically with each other. Therefore, researchers cannot treat them individually. More electrons lead to more entanglements, and thus the problem turns exponentially more challenging.

There are no exact solutions for molecules that are more complex compared to the two electrons found in a pair of hydrogen atoms. Even approximations are not so accurate when more than a few electrons are involved.

One of the difficulties is that the electronic structure of a molecule includes states for an infinite number of orbitals that move further away from the atoms. Moreover, it is not easy to differentiate one electron from another, and the same state cannot be occupied by two electrons. The latter rule is the result of exchange symmetry, which governs the consequences when identical particles change states.

Mezzacapo and the team at IBM Quantum devised a technique for reducing the number of orbitals considered and enforcing exchange symmetry. This technique is based on approaches developed for quantum computing applications and renders the problem more analogous to scenarios in which electrons are restricted to predefined locations, for example, in a rigid lattice.

The problem was made more manageable by the similarity to rigid lattices. Earlier, Carleo trained neural networks to remodel the behavior of electrons restricted to the sites of a lattice.

The researchers could propose solutions to Mezzacapos compacted problems by extending those techniques. The neural network developed by the team calculates the probability for each state. This probability can be used to predict the energy of a specific state. The molecule is the most stable in the lowest energy level, also called the equilibrium energy.

Thanks to the innovations of the researchers, the electronic structure of a basic molecule can be calculated quickly and easily. To demonstrate the accuracy of their approaches, the researchers estimated the amount of energy required to break a real-world molecule and its bonds.

The researchers performed calculations for lithium hydride (LiH), dihydrogen (H2), water (H2O), ammonia (NH3), dinitrogen (N2), and diatomic carbon (C2). The researchers estimates for all the molecules were found to be highly accurate even in ranges where current methods struggle.

The aim of the researchers is to handle larger and more complex molecules by employing more advanced neural networks. One objective is to tackle chemicals such as those found in the nitrogen cycle, where nitrogen-based molecules are made and broken by biological processes to render them usable for life.

We want this to be a tool that could be used by chemists to process these problems.

Giuseppe Carleo, Research Scientist, Center for Computational Quantum Physics, Flatiron Institute

Carleo, Choo, and Mezzacapo are not the only researchers seeking to use machine learning to handle problems in quantum chemistry. In September 2019, they first presented their study on arXiv.org. In the same month, a research group in Germany and another one at Googles DeepMind in London reported their studies that involved using machine learning to reconstruct the electronic structure of molecules.

The other two groups made use of a similar method that does not constrain the number of orbitals considered. However, this inclusiveness is more computationally laborious, a disadvantage that will only worsen when more complex molecules are involved.

Using the same computational resources, the method employed by Carleo, Choo, and Mezzacapo produces higher accuracy; however, the simplifications performed to achieve this accuracy could lead to biases.

Overall, its a trade-off between bias and accuracy, and its unclear which of the two approaches has more potential for the future. Only time will tell us which of these approaches can be scaled up to the challenging open problems in chemistry.

Giuseppe Carleo, Research Scientist, Center for Computational Quantum Physics, Flatiron Institute

Choo, K., et al. (2020) Fermionic neural-network states for ab-initio electronic structure. Nature Communications. doi.org/10.1038/s41467-020-15724-9.

Source: https://www.simonsfoundation.org/

See the article here:
New Tool Could Pave the Way for Future Insights in Quantum Chemistry - AZoQuantum

Archer CEO Dr Mohammad Choucair and quantum technology manager Dr. Martin Fuechsle

Archer Materials has announced a new agreement with IBM which it hopes will advance quantum computing and progress work towards solutions for the greater adoption of the technology.

Joining the IBM Q Network, Archer will gain access to IBM's quantum computing expertise and resources, seeing the Sydney-based company use IBM's open-source software framework, Qiskit.

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

Archer is the first Australian company that develops a quantum computing processor and hardware to join the IBM Q Network. The IBM Q Network provides access to the company's experts, developer tools, and cloud-based quantum systems through IBM Q Cloud.

"We are the first Australian company building a quantum chip to join into the global IBM Q Network as an ecosystem partner, a group of the very best organisations at the forefront of quantum computing." Archer CEO Dr Mohammad Choucair said.

"Ultimately, we want Australian businesses and consumers to be one of the first beneficiaries of this exciting technology, and now that we are collaborating with IBM, it greatly increases our chances of success".

Archer is advancing the commercial readiness of its12CQ qubit processor chip technology towards a minimum viable product.

"We look forward to working with IBM and members of the network to address the most fundamental challenges to the wide-scale adoption of quantum computing, using our potentially complementary technologies as starting points," Choucair added.

In November, Archer said it was continuing to inch towards its goal of creating a room temperature quantum computer, announcing at the time it had assembled a three qubit array.

The company said it has placed three isolated qubits on a silicon wafer with metallic control electrodes being used for measurement. Archer has previously told ZDNet it conducts measurements by doing magnetic fields sweeps at microwave frequencies.

"The arrangement of the qubits was repeatable and reproducible, thereby allowing Archer to quickly build and test working prototypes of quantum information processing devices incorporating a number of qubits; individual qubits; or a combination of both, which is necessary to meet Archer's aim of building a chip for a practical quantum computer," the company said.

In August, the company said it hadassembled its first room-temperature quantum bit.

Archer is building chip prototypes at the Research and Prototype Foundry out of the University of Sydney's AU$150 million Sydney Nanoscience Hub.

2020s are the decade of commercial quantum computing, says IBM

IBM spent a great deal of time showing off its quantum-computing achievements at CES, but the technology is still in its very early stages.

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

There are working machines today that perform some small part of what a full quantum computer may eventually do. But what are the real-world applications for quantum computing?

Quantum computing has arrived, but we still don't really know what to do with it

Even for a technology that makes a virtue of uncertainty, where quantum goes next is something of a mystery.

Quantum computing: Myths v. Realities (TechRepublic)

Futurist Isaac Arthur explains why quantum computing is a lot more complicated than classical computing.

Link:
Archer to work alongside IBM in progressing quantum computing - ZDNet

Stephen Wolfram blames himself for not changing the face of physics sooner.

I do fault myself for not having done this 20 years ago, the physicist turned software entrepreneur says. To be fair, I also fault some people in the physics community for trying to prevent it happening 20 years ago. They were successful. Back in 2002, after years of labor, Wolfram self-published A New Kind of Science, a 1,200-page magnum opus detailing the general idea that nature runs on ultrasimple computational rules. The book was an instant best seller and received glowing reviews: the New York Times called it a first-class intellectual thrill. But Wolframs arguments found few converts among scientists. Their work carried on, and he went back to running his software company Wolfram Research. And that is where things remaineduntil last month, when, accompanied by breathless press coverage (and a 448-page preprint paper), Wolfram announced a possible path to the fundamental theory of physics based on his unconventional ideas. Once again, physicists are unconvincedin no small part, they say, because existing theories do a better job than his model.

At its heart, Wolframs new approach is a computational picture of the cosmosone where the fundamental rules that the universe obeys resemble lines of computer code. This code acts on a graph, a network of points with connections between them, that grows and changes as the digital logic of the code clicks forward, one step at a time. According to Wolfram, this graph is the fundamental stuff of the universe. From the humble beginning of a small graph and a short set of rules, fabulously complex structures can rapidly appear. Even when the underlying rules for a system are extremely simple, the behavior of the system as a whole can be essentially arbitrarily rich and complex, he wrote in a blog post summarizing the idea. And this got me thinking: Could the universe work this way? Wolfram and his collaborator Jonathan Gorard, a physics Ph.D. candidate at the University of Cambridge and a consultant at Wolfram Research, found that this kind of model could reproduce some of the aspects of quantum theory and Einsteins general theory of relativity, the two fundamental pillars of modern physics.

But Wolframs models ability to incorporate currently accepted physics is not necessarily that impressive. Its this sort of infinitely flexible philosophy where, regardless of what anyone said was true about physics, they could then assert, Oh, yeah, you could graft something like that onto our model, says Scott Aaronson, a quantum computer scientist at the University of Texas at Austin.

When asked about such criticisms, Gorard agreesto a point. Were just kind of fitting things, he says. But we're only doing that so we can actually go and do a systematized search for specific rules that fit those of our universe.

Wolfram and Gorard have not yet found any computational rules meeting those requirements, however. And without those rules, they cannot make any definite, concrete new predictions that could be experimentally tested. Indeed, according to critics, Wolframs model has yet to even reproduce the most basic quantitative predictions of conventional physics. The experimental predictions of [quantum physics and general relativity] have been confirmed to many decimal placesin some cases, to a precision of one part in [10 billion], says Daniel Harlow, a physicist at the Massachusetts Institute of Technology. So far I see no indication that this could be done using the simple kinds of [computational rules] advocated by Wolfram. The successes he claims are, at best, qualitative. Further, even that qualitative success is limited: There are crucial features of modern physics missing from the model. And the parts of physics that it can qualitatively reproduce are mostly there because Wolfram and his colleagues put them in to begin with. This arrangement is akin to announcing, If we suppose that a rabbit was coming out of the hat, then remarkably, this rabbit would be coming out of the hat, Aaronson says. And then [going] on and on about how remarkable it is.

Unsurprisingly, Wolfram disagrees. He claims that his model has replicated most of fundamental physics already. From an extremely simple model, were able to reproduce special relativity, general relativity and the core results of quantum mechanics, he says, which, of course, are what have led to so many precise quantitative predictions of physics over the past century.

Even Wolframs critics acknowledge he is right about at least one thing: it is genuinely interesting that simple computational rules can lead to such complex phenomena. But, they hasten to add, that is hardly an original discovery. The idea goes back long before Wolfram, Harlow says. He cites the work of computing pioneers Alan Turing in the 1930s and John von Neumann in the 1950s, as well as that of mathematician John Conway in the early 1970s. (Conway, a professor at Princeton University, died of COVID-19 last month.) To the contrary, Wolfram insists that he was the first to discover that virtually boundless complexity could arise from simple rules in the 1980s. John von Neumann, he absolutely didnt see this, Wolfram says. John Conway, same thing.

Born in London in 1959, Wolfram was a child prodigy who studied at Eton College and the University of Oxford before earning a Ph.D. in theoretical physics at the California Institute of Technology in 1979at the age of 20. After his Ph.D., Caltech promptly hired Wolfram to work alongside his mentors, including physicist Richard Feynman. I dont know of any others in this field that have the wide range of understanding of Dr. Wolfram, Feynman wrote in a letter recommending him for the first ever round of MacArthur genius grants in 1981. He seems to have worked on everything and has some original or careful judgement on any topic. Wolfram won the grantat age 21, making him among the youngest ever to receive the awardand became a faculty member at Caltech and then a long-term member at the Institute for Advanced Study in Princeton, N.J. While at the latter, he became interested in simple computational systems and then moved to the University of Illinois in 1986 to start a research center to study the emergence of complex phenomena. In 1987 he founded Wolfram Research, and shortly after he left academia altogether. The software companys flagship product, Mathematica, is a powerful and impressive piece of mathematics software that has sold millions of copies and is today nearly ubiquitous in physics and mathematics departments worldwide.

Then, in the 1990s, Wolfram decided to go back to scientific researchbut without the support and input provided by a traditional research environment. By his own account, he sequestered himself for about a decade, putting together what would eventually become A New Kind of Science with the assistance of a small army of his employees.

Upon the release of the book, the media was ensorcelled by the romantic image of the heroic outsider returning from the wilderness to single-handedly change all of science. Wired dubbed Wolfram the man who cracked the code to everything on its cover. Wolfram has earned some bragging rights, the New York Times proclaimed. No one has contributed more seminally to this new way of thinking about the world. Yet then, as now, researchers largely ignored and derided his work. Theres a tradition of scientists approaching senility to come up with grand, improbable theories, the late physicist Freeman Dyson told Newsweek back in 2002. Wolfram is unusual in that hes doing this in his 40s.

Wolframs story is exactly the sort that many people want to hear, because it matches the familiar beats of dramatic tales from science history that they already know: the lone genius (usually white and male), laboring in obscurity and rejected by the establishment, emerges from isolation, triumphantly grasping a piece of the Truth. But that is rarelyif everhow scientific discovery actually unfolds. There are examples from the history of science that superficially fit this image: Think of Albert Einstein toiling away on relativity as an obscure Swiss patent clerk at the turn of the 20th century. Or, for a more recent example, consider mathematician Andrew Wiles working in his attic for years to prove Fermats last theorem before finally announcing his success in 1995. But portraying those discoveries as the work of a solo genius, romantic as it is, belies the real working process of science. Science is a group effort. Einstein was in close contact with researchers of his day, and Wiless work followed a path laid out by other mathematicians just a few years before he got started. Both of them were active, regular participants in the wider scientific community. And even so, they remain exceptions to the rule. Most major scientific breakthroughs are far more collaborativequantum physics, for example, was developed slowly over a quarter-century by dozens of physicists around the world.

I think the popular notion that physicists are all in search of the eureka moment in which they will discover the theory of everything is an unfortunate one, says Katie Mack, a cosmologist at North Carolina State University. We do want to find better, more complete theories. But the way we go about that is to test and refine our models, look for inconsistencies and incrementally work our way toward better, more complete models.

Most scientists would readily tell you that their discipline isand always has beena collaborative, communal process. Nobody can revolutionize a scientific field without first getting the critical appraisal and eventual validation of their peers. Today this requirement is performed through peer reviewa process Wolframs critics say he has circumvented with his announcement. Certainly theres no reason that Wolfram and his colleagues should be able to bypass formal peer review, Mack says. And they definitely have a much better chance of getting useful feedback from the physics community if they publish their results in a format we actually have the tools to deal with.

Mack is not alone in her concerns. Its hard to expect physicists to comb through hundreds of pages of a new theory out of the blue, with no buildup in the form of papers, seminars and conference presentations, says Sean Carroll, a physicist at Caltech. Personally, I feel it would be more effective to write short papers addressing specific problems with this kind of approach rather than proclaiming a breakthrough without much vetting.

So why did Wolfram announce his ideas this way? Why not go the traditional route? I don't really believe in anonymous peer review, he says. I think its corrupt. Its all a giant story of somewhat corrupt gaming, I would say. I think its sort of inevitable that happens with these very large systems. Its a pity.

So what are Wolframs goals? He says he wants the attention and feedback of the physics community. But his unconventional approachsoliciting public comments on an exceedingly long paperalmost ensures it shall remain obscure. Wolfram says he wants physicists respect. The ones consulted for this story said gaining it would require him to recognize and engage with the prior work of others in the scientific community.

And when provided with some of the responses from other physicists regarding his work, Wolfram is singularly unenthused. Im disappointed by the naivete of the questions that youre communicating, he grumbles. I deserve better.

Read the original here:
Physicists Criticize Stephen Wolfram's 'Theory of Everything' - Scientific American