Daily Archives: April 25, 2022

DUBLIN, April 25, 2022--(BUSINESS WIRE)--The "Quantum Computing Market by Technology, Infrastructure, Services, and Industry Verticals 2022 - 2027" report has been added to ResearchAndMarkets.com's offering.

This report assesses the technology, companies/organizations, R&D efforts, and potential solutions facilitated by quantum computing.

The report provides global and regional forecasts as well as the outlook for quantum computing impact on infrastructure including hardware, software, applications, and services from 2022 to 2027. This includes the quantum computing market across major industry verticals.

Quantum Computing Industry Impact

The implications for data processing, communications, digital commerce and security, and the internet as a whole cannot be overstated as quantum computing is poised to radically transform the ICT sector. In addition, quantum computing will disrupt entire industries ranging from government and defense to logistics and manufacturing. No industry vertical will be immune to the potential impact of quantum computing. Every industry must pay great attention to technology developments, implementation, integration, and market impacts.

Quantum Computing Technology Development

While there is great promise for quantum computing, it remains largely in the research and development (R&D) stage as companies, universities, and research organizations seek to solve some of the practical problems for commercialization such as how to keep a qubit stable. The stability problem is due to molecules always being in motion, even if that motion is merely a small vibration. When qubits are disturbed, a condition referred to as decoherence occurs, rendering computing results unpredictable or even useless. One of the potential solutions is to use super-cooling methods such as cryogenics.

Some say there is a need to reach absolute zero (the temperature at which all molecular motion ceases), but that is a theoretical temperature that is practically impossible to reach and maintain, requiring enormous amounts of energy. There are some room-temperature quantum computers in R&D using photonic qubits, but nothing is yet scalable. Some experts say that if the qubit energy level is high enough, cryogenic type cooling is not a requirement.

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Alternatives include ion trap quantum computing and other methods to achieve very cold super-cooled small-scale demonstration level computing platforms. There are additional issues involved with implementing and operating quantum computing. In terms of maintenance, quantum systems must be kept at subzero temperatures to keep the qubits stable, which creates trouble for people working with them and expensive, energy-consuming equipment to support.

Once these issues are overcome, we anticipate that quantum computing will become more mainstream for solving specific types of problems. However, there will remain general-purpose computing problems that must be solved with classical computing. In fact, we anticipate development of solutions that involve quantum and classical CPUs on the same computing platform, which will be capable of solving combined general purpose and use case-specific computation problems.

These next-generation computing systems will provide the best of both worlds, which will be high-speed, general-purpose computing combined with use case-specific ultra-performance for certain tasks that will remain outside the range of binary computation for the foreseeable future.

Select Report Findings:

The global market for QC hardware will exceed $8.3 billion by 2027

Leading application areas are simulation, optimization, and sampling

Managed services will reach $298 million by 2027 with CAGR of 43.9%

Key professional services will be deployment, maintenance, and consulting

QC based on superconducting (cooling) loops tech will reach $3.7B by 2027

Fastest growing industry verticals will be government, energy, and transportation

Key Topics Covered:

1.0 Executive Summary

2.0 Introduction

2.1 Understanding Quantum Computing

2.2 Quantum Computer Types

2.2.1 Quantum Annealer

2.2.2 Analog Quantum

2.2.3 Universal Quantum

2.3 Quantum Computing vs. Classical Computing

2.3.1 Will Quantum replace Classical Computing?

2.3.2 Physical Qubits vs. Logical Qubits

2.4 Quantum Computing Development Timeline

2.5 Quantum Computing Market Factors

2.6 Quantum Computing Development Progress

2.6.1 Increasing the Number of Qubits

2.6.2 Developing New Types of Qubits

2.7 Quantum Computing Patent Analysis

2.8 Quantum Computing Regulatory Analysis

2.9 Quantum Computing Disruption and Company Readiness

3.0 Technology and Market Analysis

3.1 Quantum Computing State of the Industry

3.2 Quantum Computing Technology Stack

3.3 Quantum Computing and Artificial Intelligence

3.4 Quantum Neurons

3.5 Quantum Computing and Big Data

3.6 Linear Optical Quantum Computing

3.7 Quantum Computing Business Model

3.8 Quantum Software Platform

3.9 Application Areas

3.10 Emerging Revenue Sectors

3.11 Quantum Computing Investment Analysis

3.12 Quantum Computing Initiatives by Country

4.0 Quantum Computing Drivers and Challenges

4.1 Quantum Computing Market Dynamics

4.2 Quantum Computing Market Drivers

4.2.1 Growing Adoption in Aerospace and Defense Sectors

4.2.2 Growing investment of Governments

4.2.3 Emergence of Advance Applications

4.3 Quantum Computing Market Challenges

5.0 Quantum Computing Use Cases

5.1 Quantum Computing in Pharmaceuticals

5.2 Applying Quantum Technology to Financial Problems

5.3 Accelerate Autonomous Vehicles with Quantum AI

5.4 Car Manufacturers using Quantum Computing

5.5 Accelerating Advanced Computing for NASA Missions

6.0 Quantum Computing Value Chain Analysis

6.1 Quantum Computing Value Chain Structure

6.2 Quantum Computing Competitive Analysis

6.2.1 Leading Vendor Efforts

6.2.2 Start-up Companies

6.2.3 Government Initiatives

6.2.4 University Initiatives

6.2.5 Venture Capital Investments

6.3 Large Scale Computing Systems

7.0 Company Analysis

7.1 D-Wave Systems Inc.

7.2 Google Inc.

7.3 Microsoft Corporation

7.4 IBM Corporation

7.5 Intel Corporation

7.6 Nokia Corporation

7.7 Toshiba Corporation

7.8 Raytheon Company

7.9 Other Companies

7.9.1 1QB Information Technologies Inc.

7.9.2 Cambridge Quantum Computing Ltd.

7.9.3 QC Ware Corp.

7.9.4 MagiQ Technologies Inc.

7.9.5 Rigetti Computing

7.9.6 Anyon Systems Inc.

7.9.7 Quantum Circuits Inc.

7.9.8 Hewlett Packard Enterprise

7.9.9 Fujitsu Ltd.

7.9.10 NEC Corporation

7.9.11 SK Telecom

7.9.12 Lockheed Martin Corporation

7.9.13 NTT Docomo Inc.

7.9.14 Alibaba Group Holding Limited

7.9.15 Booz Allen Hamilton Inc.

7.9.16 Airbus Group

7.9.17 Amgen Inc.

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Global Quantum Computing Market Analysis Report 2022-2027: Assessment of Technology, Companies/Organizations, R&D Efforts, and Potential Solutions -...

It is true that adversaries are collecting our encrypted data today so they can decrypt it later. In essence anything sent using PKI (Public Key Infrastructure) today may very well be decrypted when quantum computing becomes available. Our recent report identifies the risk to account numbers and other long tail data (data that still has high value 5 years or more into the future). Data you send today using traditional PKI is the horse that left the barn.

But this article describes a scary scenario where an adversarys quantum computer hacks the US militarys communications and utilizes that advantage to sink the US Fleet but that is highly unlikely as long as government agencies follow orders. The US government specifies that AES-128 be used for secret (unclassified) information and AES-256 for top secret (classified) information. While AES-128 can be cracked using quantum computers, one estimate suggests that would take 6 months of computing time. That would be very expensive. Most estimates indicate that using AES-256 would take hundreds of years, but the military is already planning an even safer alternative it just isnt yet in production (that I am aware of):

Arthur Herman conducted two formidable studies on what a single, successful quantum computing attack would do to both our banking systems and a major cryptocurrency. A single attack on the banking system by a quantum computer would take down Fedwire and cause $2 trillion of damage in a very short period of time. A similar attack on a cryptocurrency like bitcoin would cause a 90 percent drop in price and would start a three-year recession in the United States. Both studies were backed up by econometric models using over 18,000 data points to predict these cascading failures.

Another disastrous effect could be that an attacker with a CRQC could take control of any systems that rely on standard PKI. So, by hacking communications, they would be able to disrupt data flows so that the attacker could take control of a device, crashing it into the ground or even using it against an enemy. Think of the number of autonomous vehicles that we are using both from a civilian and military standpoint. Any autonomous devices such as passenger cars, military drones, ships, planes, and robots could be hacked by a CRQC and shut down or controlled to perform activities not originally intended by the current users or owners.

Overview byTim Sloane,VP, Payments Innovation at Mercator Advisory Group

Originally posted here:

Quantum Isnt Armageddon; But Your Horse Has Already Left the Barn - PaymentsJournal

Under normal circumstances, the arrival of a breakthrough technology creates its own hype. The first light bulb drew hundreds of gawkers to Menlo Park, New Jersey, in 1879. A century later, crowds gathered again to see artificial-intelligence-powered supercomputers defeat human grandmasters at chess and Go. Social media announced its arrival with hockey-stick growth. When you discover something that actually has the power to transform the world, the world usually takes notice.

But investors in quantum computing a technology that has the theoretical potential to make a traditional supercomputer look like a slide rule have been mostly tooting their own horns, promising a revolution that's just over the horizon. The biggest booster is IBM, which has been pushing quantum through YouTube videos and sponsored podcasts. The breaking of the "100-qubit processor barrier" in November, a feat performed by IBM's Eagle chip, may not have rocked the tech world, but you wouldn't know it from the company's online rollout.

"Dreams are time travel to the future," gushed Daro Gil, IBM's director of research, in a video announcing Eagle. "We have definitely traveled into the future," he continued, adding: "This is the real thing."

IBM has a big financial incentive to hype its quantum potential. With Eagle, the company is betting that quantum computing can return it to the first ranks of tech giants, alongside Google, Microsoft, and Amazon, all of which are bankrolling their own quantum efforts. Smaller quantum players are also scrambling to break into the space. IonQ, which went public last year through a special-purpose acquisition company, trades at 1,000 times its annual revenue. D-Wave, which has backing from Goldman Sachs and Jeff Bezos, also plans to go public through a SPAC . One recent report estimates that quantum computers could generate nearly a trillion dollars in annual revenue by 2050, with applications from auto and airplane manufacturing to pharmaceutical development and finance.

But the hype about quantum computing's future glosses over the limitations of its present. For now, quantum computers remain exceedingly slow and buggy to the point of uselessness. Unlike AI and augmented reality, which already enjoy robust pipelines of products heading to market, quantum computing lacks anything close to a working prototype with the power to draw a crowd. Both IBM and IonQ have "road maps" that promise an operational 1,000-qubit processor by the end of next year. But experts agree that even if the companies manage to hit that significant-sounding target and that's a big if a versatile quantum computer that can perform a range of practical operations on its own, outside a lab, is still many years away.

"It's going to be a lot of gradual improvement in capabilities," says Celia Merzbacher, who heads up the Quantum Economic Development Consortium. "There's a lot that has to happen to get to something that resembles what we think of today as a computer."

Which leads to the question most of us have about quantum computing: What the hell is it?

For decades, the fundamental unit of computing has been the "bit" either a one or a zero. Charles Babbage's mechanical computers used the position of gears and levers to record bits. On a flash drive, bits are stored as electrical charges on tiny magnetic cells.

Quantum computing, by contrast, operates on qubits, which can be a one, a zero, or a combination of both an uncanny, ambiguous state known as superposition. This is possible because subatomic particles defy common sense, appearing and disappearing in ways that continue to surprise and baffle physicists. Photons, to give just one example, form a pattern with light and dark bands when shot through a barrier with two slits. But try using a detector to observe which slit an individual photon passed through, and the pattern disappears.

The seductive promise of the qubit lies in its exponential power. Two regular bits can be used to represent four states 00, 01, 10, 11 but only one of those states at any given time. In theory, two qubits could represent all four states at the same time and then resolve to whichever state is needed to solve a given problem. That means the 127-qubit Eagle has a computing potential that is thousands of millions of millions times that of a classical supercomputer.

The problem is, it's incredibly difficult to get all those qubits working together. With today's technology, maintaining a state of superposition within even one qubit is a tall order. Subatomic particles are sensitive to tiny changes in their environment. Scientists have tried to stabilize their quantum processors by storing them at extremely cold temperatures, but it hasn't made much difference. So for now, quantum computing depends on a subdiscipline called "quantum error correction," which usually involves running the same code over and over again, through multiple qubits, and using probability to correct for random errors.

The need for error correction has led scientists to distinguish between physical qubits, like the ones that make up the Eagle, and more idealized logical qubits, which are sufficiently reliable to program with. By most estimates, it takes 1,000 physical qubits to yield one logical qubit. So even if IBM hits its 1,000-qubit benchmark by next year, it will have succeeded in achieving only the computing capability of a single traditional bit a computer with a fraction of the power of a video-game console from the 1980s.

Sankar Das Sarma, a theoretical physicist at the University of Maryland who has published widely on quantum computing, believes that the technology is real and has tremendous long-term potential. But he is skeptical about its short-term prospects.

"Claiming to have a thousand or a trillion qubits by some deadline is a meaningless statement unless the properties of those 'qubits' have extremely well-defined technical specifications," he told me. "One can easily have as many qubits as one wants, if they are sufficiently bad from a computational viewpoint."

The need for a new, more powerful computer model is certainly real enough. For decades, as predicted by Moore's law, computer power has been growing at an exponential rate. But that growth has begun to slow, hemmed in by physical reality. In simple terms, we're reaching the limit of how many transistors we can pack onto the chips that power classical computers. And if those transistors can't get smaller, the electrical signals that zip around on the chips can't get any faster. Our computers are still getting smarter and speedier, but those gains are beginning to level off.

But qubits aren't constrained by traditional limits of space and time. They exist in multiple states simultaneously meaning, at least in theory, that we can deploy vast armies of them to do our computational bidding, if we can figure out how to harness their shifty nature.

As is often the case, two primary applications are driving the new technology: surveillance and finance. As more and more data is protected by dual-key encryption, governments are eager to find a way to crack the code. That requires figuring out the factors of very large semiprime numbers a problem that would take the most powerful classical computers billions of years. A quantum processor with thousands of logical, error-corrected qubits, by contrast, could conceivably decrypt emails and other communications almost instantly, enabling governments to decode and read messages while they were still in transit. Many countries are said to be storing petabytes of encrypted data that was transmitted by their adversaries, in the hope that quantum computing will one day render it all legible.

At the same time, the US is working to build a standard for "post-quantum cryptography" that can survive a qubit attack. "It is not unreasonable to think we'll have total chaos," says Miles Taylor, who helped organize the effort as chief of staff at the Department of Homeland Security. "Someone will have a massive asymmetric advantage. It could be IBM. It could be the Chinese Communist Party." When I asked about timelines, Taylor said he believed we'd see a quantum computer capable of cracking current encryption technologies "within a decade."

Another sector that has been betting heavily on quantum's potential is finance. D-Wave, the firm with backing from Goldman Sachs, is marketing portfolio-optimization services to finance companies, promising higher returns at lower risk. Classical computers have trouble quickly solving what are known as "combinatorial optimization" problems, such as how best to allocate investments in a variety of scenarios. One analyst, for example, reported that classical computers took a month to run a detailed tail-risk simulation on the effects of a low-probability catastrophe on the markets.

Another real-world application in this category is the so-called traveling-salesman problem, which seeks to calculate the shortest possible route from city to city an area with obvious applications for delivery logistics and military supply lines. Last year, when the Australian Army used quantum computing to test its systems against known logistics challenges, one military leader cautioned that the technology was still in the "prototype stage" and that quantum computers remained "too small and fragile to give useful solutions."

Even the limited successes attributed to quantum computers aren't always as revolutionary as they seem. Many quantum computers including D-Wave's portfolio optimizers are "hybrid" machines that work in tandem with classical computers. The same is true of almost all of the quantum-computing power that is publicly accessible via the cloud. In some cases, it amounts to little more than sprinkling of quantum dust on problems that are teed up, coded, and transmitted by classical machines. The bit does all the heavy lifting, and qubit gets the credit.

The field is also plagued by a lack of agreement over basic definitions. In 2019, Google and IBM clashed over whether Google's Sycamore processor had truly achieved "quantum supremacy" by performing a tailor-made computational task in three minutes. Google insisted it would take a classical computer thousands of years to complete the same task. IBM argued it would take only days.

If your business is raising capital for quantum startups, however, such issues are often dismissed as petty details. Quantum computing arose in a culture of initial public offerings that has traditionally been eager to bet big on high-risk, high-reward technology. It often cloaks itself in the accoutrements of established scientific enterprises subzero chambers, scientists pacing around labs, university partnerships, huge research budgets without having any hard-earned results to show for itself. Like the qubit itself, the future of quantum computing remains highly theoretical.

Das Sarma, the physicist, compares current quantum efforts to trying to build a smartphone from hundred-year-old vacuum tubes. The basic principles may be in place, but the engineering hasn't had time to catch up. As a result, quantum computing, like its earliest predecessors, could remain in a rudimentary state for a long time to come. "The Egyptian abacus was actually a computer," Das Sarma observes. "But not a particularly good one."

Mattathias Schwartz is a senior correspondent at Insider.

Originally posted here:

Is Quantum Computing the Next Big Thing? - Business Insider

PsiQuantum, founded in 2016 by four researchers with roots at Bristol University, Stanford University, and York University, is one of a few quantum computing startups thats kept a moderately low PR profile. (Thats if you disregard the roughly $700 million in funding it has attracted.) The main reason is PsiQuantum has eschewed the clamorous public chase for NISQ (near-term intermediate scale quantum) computers and set out to develop a million-qubit system the company says will deliver big gains on big problems as soon as it arrives.

When will that be?

PsiQuantum says it will have all the manufacturing processes in place by the middle of the decade and its working closely with GlobalFoundries (GF) to turn its vision into reality. The generous size of its funding suggests many think it will succeed. PsiQuantum is betting on a photonics-based approach called fusion-based quantum computing (paper) that relies mostly on well-understood optical technology but requires extremely precise manufacturing tolerances to scale up. It also relies on managing individual photons, something that has proven difficult for others.

Heres the companys basic contention:

Success in quantum computing will require large, fault-tolerant systems and the current preoccupation with NISQ computers is an interesting but ultimately mistaken path. The most effective and fastest route to practical quantum computing will require leveraging (and innovating) existing semiconductor manufacturing processes and networking thousands of quantum chips together to reach the million-qubit system threshold thats widely regarded as necessary to run game-changing applications in chemistry, banking, and other sectors.

Its not that incrementalism is bad. In fact, its necessary. But its not well served when focused on delivering NISQ systems argues Peter Shadbolt, one of PsiQuantum founders and the current chief scientific officer.

Conventional supercomputers are already really good. Youve got to do some kind of step change, you cant increment your way [forward], and especially you cant increment with five qubits, 10 qubits, 20 qubits, 50 qubits to a million. That is not a good strategy. But its also not true to say that were planning to leap from zero to a million, said Shadbolt. We have a whole chain of incrementally larger and larger systems that were building along the way. Those allow us to validate the control electronics, the systems integration, the cryogenics, the networking, etc. But were not spending time and energy, trying to dress those up as something that theyre not. Were not having to take those things and try to desperately extract computational value from something that doesnt have any computational value. Were able to use those intermediate systems for our own learnings and for our own development.

Thats a much different approach from the majority of quantum computing hopefuls. Shadbolt suggests the broad message about the need to push beyond NISQ dogma is starting to take hold.

There is a change that is happening now, which is that people are starting to program for error-corrected quantum computers, as opposed to programming for NISQ computers. Thats a welcome change and thats happening across the whole space. If youre programming for NISQ computers, you very rapidly get deeply entangled if youll forgive the pun with the hardware. You start looking under the hood, and you start trying to find shortcuts to deal with the fact that you have so few gates at your disposal. So, programming NISQ computers is a fascinating, intellectually stimulating activity, Ive done it myself, but it rapidly becomes sort of siloed and you have to pick a winner, said Shadbolt.

With fault tolerance, once you start to accept that youre going to need error correction, then you can start programming in a fault-tolerant gate set which is hardware agnostic, and its much more straightforward to deal with. There are also some surprising characteristics, which mean that the optimizations that you make to algorithms in a fault-tolerant regime are in many cases, the diametric opposite of the optimizations that you would make in the NISQ regime. It really takes a different approach but its very welcome that the whole industry is moving in that direction and spending less time on these kinds of myopic, narrow efforts, he said.

That sounds a bit harsh. PsiQuantum is no doubt benefitting from the manifold efforts by the young quantum computing ecosystem to tout advances and build traction by promoting NISQ use cases. Theres an old business axiom that says a little hype is often a necessary lubricant to accelerate development of young industries; quantum computing certainly has its share. A bigger question is will PsiQuantum beat rivals to the end-game? IBM has laid out a detailed roadmap and said 2023 is when it will start delivering quantum advantage, using a 1000-qubit system, with plans for eventual million-qubit systems. Intel has trumpeted its CMOS strength to scale up manufacturing its quantum dot qubits. D-Wave has been selling its quantum annealing systems to commercial and government customers for years.

Its really not yet clear which of the qubit technologies semiconductor-based superconducting, trapped ions, neutral atoms, photonics, or something else will prevail and for which applications. Whats not ambiguous is PsiQuantums Go Big or Go Home strategy. Its photonics approach, argues the company, has distinct advantages in manufacturability and scalability, operating environment (less frigid), ease of networking, and error correction. Shadbolt recently talked with HPCwire about the companys approach, technology and progress.

What is fusion-based quantum computing?

Broadly, PsiQuantum uses a form of linear optical quantum computing in which individual photons are used as qubits. Over the past year and a half, the previously stealthy PsiQuantum has issued several papers describing the approach while keeping many details close to the vest (papers listed at end of article). The computation flow is to generate single photons and entangle them. PsiQuantum uses dual rail entangling/encoding for photons. The entangled photons are the qubits and are grouped into what PsiQuantum calls resource states, a group of qubits if you will. Fusion measurements (more below) act as gates. Shadbolt says the operations can be mapped to a standard gate-set to achieve universal, error-corrected, quantum computing.

On-chip components carry out the process. It all sounds quite exotic, in part because it differs from more-widely used matter-based qubit technologies. The figure below taken from a PsiQuantum paper Fusion-based quantum computation issued about a year ago roughly describes the process.

Digging into the details is best served by reading the papers and the company has archived videos exploring its approach on its website. The video below is a good brief summation by Mercedes Gimeno-Segovia, vice president of quantum architecture at PsiQuantum.

Shadbolt also briefly described fusion-based quantum computation (FBQC).

Once youve got single photons, you need to build what we refer to as seed states. Those are pretty small entangled states and can be constructed again using linear optics. So, you take some single photons and send them into an interferometer and together with single photon detection, you can probabilistically generate small entangled states. You can then multiplex those again and basically the task is to get as fast as possible to a large enough, complex enough, appropriately structured, resource state which is ready to then be acted upon by a fusion network. Thats it. You want to kill the photon as fast as possible. You dont want photons living for a long time if you can avoid it. Thats pretty much it, said Shadbolt.

The fusion operators are the smallest simplest piece of the machine. The multiplex, single-photon sources are the biggest, most expensive piece. Everything in the middle is kind of the secret sauce of our architecture, some of that weve put out in that paper and you can see kind of how that works, he said. (At the risk of overkill, another brief description of the system from PsiQuantum is presented at the end of the article.)

One important FBQC advantage, says PsiQuantum, is that the shallow depth of optical circuits make error correction easier. The small entangled states fueling the computation are referred to as resource states. Importantly, their size is independent of code distance used or the computation being performed. This allows them to be generated by a constant number of operations. Since the resource states will be immediately measured after they are created, the total depth of operations is also constant. As a result, errors in the resource states are bounded, which is important for fault-tolerance.

Some of the differences between the PsiQuantums FBQC design and the more familiar MBQC (measurement-based quantum computing) paradigm are shown below.

Another advantage is the operating environment.

Nothing about photons themselves requires cryogenic operation. You can do very high fidelity manipulation and generation of qubits at room temperature, and in fact, you can even detect single photons at room temperature just fine. The efficiency of room temperature single photon detectors, is not good enough for fault tolerance. These room temperature detectors are based on pretty complex semiconductor devices, avalanche photodiodes, and theres no physical reason why you couldnt push those to the necessary efficiency, but it looks really difficult [and] people have been trying for a very long time, said Shadbolt

We use a superconducting single-photon detector, which can achieve the necessary efficiencies without a ton of development. Its worth noting those detectors run in the ballpark of 4 Kelvin. So liquid helium temperature, which is still very cold, but its nowhere near as cold as milli-Kelvin temperatures required for superconducting qubits or some of the competing technologies, said Shadbolt.

This has important implications for control circuit placement as well as for reduced power needed to generate the 4-degree Kelvin environment.

Theres a lot to absorb here and its best done directly from the papers. PsiQuantum, like many other quantum start-ups, was founded by researchers who were already digging into the quantum computing space and theyve shown that PsiQuantums FBQC flavor of linear optical quantum computing will work. While at Bristol, Shadbolt was involved in the first demonstration of running a Variational Quantum Eigensolver (VQE) on a photonic chip.

The biggest challenges for PsiQuantum, he suggests, are developing manufacturing techniques and system architecture around well-known optical technology. The company argues having a Tier-1 fab partner such as GlobalFoundries is decisive.

You can go into infinite detail on the architecture and how all the bits and pieces go together. But the point of optical quantum computing is that the network of components is pretty complicated all sorts of modules and structures and multiplexing strategies, and resource state generation schemes and interferometers, and so on but theyre all just made out of beam splitters, and switches, and single photon sources and detectors. Its kind of like in a conventional CPU, you can go in with a microscope and examine the structure of the cache and the ALU and whatever, but underneath its all just transistors. Its the same kind of story here. The limiting factor in our development is the semiconductor process enablement. The thesis has always been that if you tried to build a quantum computer anywhere other than a high-volume semiconductor manufacturing line, your quantum computer isnt going to work, he said.

Any quantum computer needs millions of qubits. Millions of qubits dont fit on a single chip. So youre talking about heaps of chips, probably billions of components realistically, and they all need to work and they all need to work better than the state of the art. That brings us to the progress, which is, again, rearranging those various components into ever more efficient and complex networks in pretty close analogy with CPU architecture. Its a very key part of our IP, but its not rate limiting and its not terribly expensive to change the network of components on the chip once weve got the manufacturing process. Were continuously moving the needle on that architecture development and weve improved these architectures in terms of their tolerance to loss by more than 150x, [actually] well beyond that. Weve reduced the size of the machine, purely through architectural improvements by many, many orders of magnitude.

The big, expensive, slow pieces of the development are in being able to build high quality components at GlobalFoundries in New York. What weve already done there is to put single photon sources and superconducting nanowire, single photon detectors into that manufacturing process engine. We can build wafers, 300-millimeter wafers, with tens of thousands of components on the wafer, including a full silicon photonics PDK (process design kit), and also a very high performing single photon detector. Thats real progress that brings us closer to being able to build a quantum computer, because that lets us build millions to billions of components.

Shadbolt says real systems will quickly follow development of the manufacturing process. PsiQuantum, like everyone in the quantum computing community, is collaborating closely with potential users. Roughly a week ago, it issued a joint paper with Mercedes-Benz discussing quantum computer simulation of Li-ion chemistry. If the PsiQuantum-GlobalFoundries process is ready around 2025, can a million-qubit system (100 logical qubits) be far behind?

Shadbolt would only say that things will happen quickly once the process has been fully developed. He noted there are three ways to make money with a quantum computer: sell machines, sell time, and sell solutions that come from that machine. I think we were exploring all of the above, he said.

Our customers, which is a growing list at this point pharmaceutical companies, car companies, materials companies, big banks are coming to us to understand what a quantum computer can do for them. To understand that, what we are doing, principally, is fault-tolerant resource counting, said Shadbolt. So that means were taking the algorithm or taking the problem the customer has, working with their technical teams to look under the hood, and understand the technical requirements of solving that problem. We are turning that into the quantum algorithms and sub routines that are appropriate. Were compiling that for the fault-tolerant gate set that will run on top of that fusion network, which by the way is a completely vanilla textbook fault-tolerant gate set.

Stay tuned.

PsiQuantum Papers

Fusion-based quantum computation, https://arxiv.org/abs/2101.09310

Creation of Entangled Photonic States Using Linear Optics, https://arxiv.org/abs/2106.13825

Interleaving: Modular architectures for fault-tolerant photonic quantum computing, https://arxiv.org/abs/2103.08612

Description of PsiQuantums Fusion-Based System from the Interleaving Paper

Useful fault-tolerant quantum computers require very large numbers of physical qubits. Quantum computers are often designed as arrays of static qubits executing gates and measurements. Photonic qubits require a different approach. In photonic fusion-based quantum computing (FBQC), the main hardware components are resource-state generators (RSGs) and fusion devices connected via waveguides and switches. RSGs produce small entangled states of a few photonic qubits, whereas fusion devices perform entangling measurements between different resource states, thereby executing computations. In addition, low-loss photonic delays such as optical fiber can be used as fixed-time quantum memories simultaneously storing thousands of photonic qubits.

Here, we present a modular architecture for FBQC in which these components are combined to form interleaving modules consisting of one RSG with its associated fusion devices and a few fiber delays. Exploiting the multiplicative power of delays, each module can add thousands of physical qubits to the computational Hilbert space. Networks of modules are universal fault-tolerant quantum computers, which we demonstrate using surface codes and lattice surgery as a guiding example. Our numerical analysis shows that in a network of modules containing 1-km-long fiber delays, each RSG can generate four logical distance-35 surface-code qubits while tolerating photon loss rates above 2% in addition to the fiber-delay loss. We illustrate how the combination of interleaving with further uses of non-local fiber connections can reduce the cost of logical operations and facilitate the implementation of unconventional geometries such as periodic boundaries or stellated surface codes. Interleaving applies beyond purely optical architectures, and can also turn many small disconnected matter-qubit devices with transduction to photons into a large-scale quantum computer.

Slides/Figures from various PsiQuantum papers and public presentations

View original post here:

PsiQuantum's Path to 1 Million Qubits by the Middle of the Decade - HPCwire

Can quantum computing hold the ultimate power to meet sustainable development?

Quantum computing has started gaining popularity with the integration of quantum mechanics through smart quantum computers. Yes, it can transform conventional computers with a highly complex nature. Meanwhile, quantum computing is ready to have the key to protecting the environment with technology. Lets celebrate Earth Day 2022 with sustainable development through quantum computing. Quantum computers hold the substantial potential to save the environment with technology and physics law. Thus, lets dig deeper into quantum computing to look out for ways how it holds the key to protecting the environment.

Earth Day 2022 is celebrated across the world to raise the awareness of environmental issues to human beings. It helps to come up with ideas to reduce the carbon footprint and energy consumption for effective sustainable development. Hence, quantum computing is determined to be the protector of the environment with technology to look out for sustainable development efficiently and effectively.

Quantum computers are a form of supercomputers with thousands of GPU and CPU cores with multiple high degrees of complex issues. It is used for performing multiple quantum calculations with Qubits for simulating the problems that human beings or classical computers cannot solve within a short period of time.

Now in the 21st century with the advancements in technologies, quantum computing can power sustainable development with smart functionalities. Quantum computers can protect the environment with technology by capturing carbon as well as fighting climate change for global warming.

Quantum computing can simulate large complicated molecules which can discover new catalysts for capturing sufficient carbon from the current environment. The room-temperature superconductors hold the key to decreasing the 10% of energy production that is lost in transmission. It will help in better processes to feed the increasing population as well as efficient batteries.

Quantum computing is set to address global challenges, raise awareness, generate solutions, and meet the sustainable development goals on Earth Day 2022. Quantum computers are transforming the illusion into reality with better climate models to protect the environment with technology. It is ready to provide sufficient in-depth insights into how the ways and activities of human beings are drastically affecting the environment and creating a barrier to sustainable development.

Multiple 200 Qubits quantum computers can help to find a catalyst to utilize the 3-5% of the worlds gas production as well as 1-2% of annual energy levels through multiple different tasks. It can be used to generate different catalysts for capturing carbon footprint from the air and decreasing carbon emissions by 80%-90%. Thus, quantum computing can control the rapid rise in temperature in the environment with technology.

That being said, lets celebrate Earth Day 2022 with quantum computing helping the world in ensuring carbon dioxide recycling and reducing harmful emissions of carbon monoxide.

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Earth Day 2022: Quantum Computing has the Key to Protect Environment! - Analytics Insight

Quantum Computing Market 2022-2028

A New Market Study, Titled Quantum Computing Market Upcoming Trends, Growth Drivers and Challenges has been featured on fusionmarketresearch.

Description

This global study of theQuantum ComputingMarketoffers an overview of the existing market trends, drivers, restrictions, and metrics and also offers a viewpoint for important segments. The report also tracks product and services demand growth forecasts for the market. There is also to the study approach a detailed segmental review. A regional study of the globalQuantum Computingindustryis also carried out in North America, Latin America, Asia-Pacific, Europe, and the Near East & Africa.The report mentions growth parameters in the regional markets along with major players dominating the regional growth.

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This research covers COVID-19 impacts on the upstream, midstream and downstream industries. Moreover, this research provides an in-depth market evaluation by highlighting information on various aspects covering market dynamics like drivers, barriers, opportunities, threats, and industry news & trends. In the end, this report also provides in-depth analysis and professional advices on how to face the post COIVD-19 period.

The research methodology used to estimate and forecast this market begins by capturing the revenues of the key players and their shares in the market. Various secondary sources such as press releases, annual reports, non-profit organizations, industry associations, governmental agencies and customs data, have been used to identify and collect information useful for this extensive commercial study of the market. Calculations based on this led to the overall market size. After arriving at the overall market size, the total market has been split into several segments and subsegments, which have then been verified through primary research by conducting extensive interviews with industry experts such as CEOs, VPs, directors, and executives. The data triangulation and market breakdown procedures have been employed to complete the overall market engineering process and arrive at the exact statistics for all segments and subsegments.

Leading players of Quantum Computing including:D-Wave SolutionsIBMGoogleMicrosoftRigetti ComputingIntelOrigin Quantum Computing TechnologyAnyon Systems Inc.Cambridge Quantum ComputingAirbus GroupNokia Bell LabsAlibaba Group HoldingToshiba

Quantum Computing Market split by Type, can be divided into:HardwareSoftwareCloud Services

Quantum Computing Market split by Application, can be divided into:Space and DefenseGovernmentAutomotiveManufacturing & LogisticsBanking and Finance

Quantum Computing Market split by Sales Channel, can be divided into:Direct ChannelDistribution Channel

Market segment by Region/Country including:North America (United States, Canada and Mexico)Europe (Germany, UK, France, Italy, Russia and Spain etc.)Asia-Pacific (China, Japan, Korea, India, Australia and Southeast Asia etc.)South America (Brazil, Argentina and Colombia etc.)Middle East & Africa (South Africa, UAE and Saudi Arabia etc.)

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Table of Contents

Chapter 1 Quantum ComputingMarket Overview 1.1 Quantum Computing Definition1.2 Global Quantum Computing Market Size Status and Outlook (2016-2030)1.3 Global Quantum Computing Market Size Comparison by Region (2016-2030)1.4 Global Quantum Computing Market Size Comparison by Type (2016-2030)1.5 Global Quantum Computing Market Size Comparison by Application (2016-2030)1.6 Global Quantum Computing Market Size Comparison by Sales Channel (2016-2030)1.7 Quantum Computing Market Dynamics (COVID-19 Impacts)1.7.1 Market Drivers/Opportunities1.7.2 Market Challenges/Risks1.7.3 Market News (Mergers/Acquisitions/Expansion)1.7.4 COVID-19 Impacts1.7.5 Post-Strategies of COVID-19

Chapter 2 Quantum ComputingMarket Segment Analysis by Player 2.1 Global Quantum Computing Sales and Market Share by Player (2019-2021)2.2 Global Quantum Computing Revenue and Market Share by Player (2019-2021)2.3 Global Quantum Computing Average Price by Player (2019-2021)2.4 Players Competition Situation & Trends2.5 Conclusion of Segment by Player

Chapter 3 Quantum ComputingMarket Segment Analysis by Type 3.1 Global Quantum Computing Market by Type3.1.1 Hardware3.1.2 Software3.1.3 Cloud Services3.2 Global Quantum Computing Sales and Market Share by Type (2016-2021)3.3 Global Quantum Computing Revenue and Market Share by Type (2016-2021)3.4 Global Quantum Computing Average Price by Type (2016-2021)3.5 Leading Players of Quantum Computing by Type in 20213.6 Conclusion of Segment by Type

Chapter 4 Quantum ComputingMarket Segment Analysis by Application 4.1 Global Quantum Computing Market by Application4.1.1 Space and Defense4.1.2 Government4.1.3 Automotive4.1.4 Manufacturing & Logistics4.1.5 Banking and Finance4.2 Global Quantum Computing Revenue and Market Share by Application (2016-2021)4.3 Leading Consumers of Quantum Computing by Application in 20214.4 Conclusion of Segment by Application

Chapter 5 Quantum ComputingMarket Segment Analysis by Sales Channel 5.1 Global Quantum Computing Market by Sales Channel5.1.1 Direct Channel5.1.2 Distribution Channel5.2 Global Quantum Computing Revenue and Market Share by Sales Channel (2016-2021)5.3 Leading Distributors/Dealers of Quantum Computing by Sales Channel in 20215.4 Conclusion of Segment by Sales Channel

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Quantum Computing Market, Growth, Share, Size, Segmentations, Industry Trends, Demand and Forecasts 2022 to 2028 - Digital Journal

Officials at the Bank of Canada (BoC) are turning their attention to crypto research in a big way.

In line with the general stance held by central banks, the BoC has, in the past, voiced concerns about cryptocurrency. For example, two years ago, the BoC published a paper highlighting the risks faced by cryptocurrency users.

Two further recent studies conducted by the BoC, one on awareness and ownership, and the other, a quantum computing simulation, yielded predictable results.

In a report titled Bitcoin Awareness, Ownership and Use: 2016-20, BoC researchers conducted a study of 12,487 participants over five years.

They found that awareness and ownership of Bitcoin have increased since 2016. Analyzing the results further, researchers noted that awareness had stabilized from 2018 to 2020. The same pattern and trend were also noted concerning Bitcoin ownership.

Cross-referencing their findings with participants demographic profiles, researchers said with increased awareness and ownership in the latter period of the study, Bitcoin ownership concentrated among young, educated men with high household income and low financial literacy.

Researchers also found more than 20% of Bitcoin owners showed low Bitcoin knowledge in failing to answer any of the three knowledge questions. From that, they deduced many owners were hit-and-run investors.

Given that investment was the most common reason owners cited for owning Bitcoin, we see that many owners may be trying to profit from cryptocurrencies without fully understanding the technology.

It concluded that ownership and use of Bitcoin and other cryptocurrencies remain low. But its still important to monitor trends in this sector to allow for informed policy-making decisions.

In collaboration with Multiverse Computing, a quantum computing company, the BoC ran stimulation models on complex financial problems, including a simulation of crypto adoption.

Sam Mugel, the CTO at Multiverse Computing, said such a simulation is not possible using classical computers. The number of possible configurations, even with just a ten-person network, is mind-bogglingly large.

Quantum computing leverages quantum theory to run complex calculations and problems as well as assess probabilities. The computers can process exponentially more data than traditional computing models.

The results found that for some industries, crypto would operate alongside traditional banking as a payment mechanism. But the degree to which individual financial institutions adopt digital assets depends on how these institutions respond to crypto adoption and the economic costs of crypto trading.

Meanwhile, the Bank of England, through its Prudential Regulation Authority (PRA) arm, which is tasked with managing financial sector risk, said it was expanding its workforce by recruiting an additional 100 staff members.

PRA Deputy Governor and Head Sam Woods said the drive was necessary to handle new policy responsibilities, including risks associated with crypto assets.

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