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Rarely are things just as they seem, and the world of quantum computing lends itself better than most to that description. Described as a fundamental shift in our processing capabilities, quantum computing's development has accelerated incredibly in the past few years. Yet according to a research paper published in the journal of the Association for Computing Machinery, relevant quantum computing (the one that's usually referred to as running circles around even the most powerful classical computers) still requires groundbreaking discoveries in a number of areas before it can dethrone a mere graphics card.

The most surprising element in the paper is the conclusion that a number of applications will remain better suited for classical computing (rather than quantum computing) for longer than previously thought. The researchers say this is true even for quantum systems running across more than a million physical qubits, whose performance the team simulated as part of their research.

Considering how today's top system, IBM's Osprey, still "only" packs in 433 qubits (with an IBM-promised 4,158-qubit system launch for 2025), the timescale towards a million qubits extends further ahead than expected.

The problem, say the researchers, is not with the applications or workloads themselves drug discovery, materials sciences, scheduling, and optimization problems in general are still very much in quantum computing's crosshairs. The issue is with the quantum computing systems themselves their architectures, and their current and future inability to intake the egregious amounts of data some of these applications require before a solution is even found. It's a simple I/O problem, not unlike the one we all knew from before NVMe SSDs became the norm, when HDDs bottlenecked CPUs and GPUs left and right: data can only be fed so quickly.

Yet how much data is sent, how fast it reaches its destination, and how long it takes to process are all elements of the same equation. In this case, the equation is for quantum advantage the moment where quantum computers offer performance that's beyond anything possible for classical systems. And it seems that in workloads that require the processing of large datasets, quantum computers will have to watch as GPUs such as Nvidia's A100 run by likely for a long, long while.

Quantum computing might have to make do with solving big compute problems on small data, while classical will have the unenviable task of processing the "big data" problems a hybrid approach to quantum computing that's been gaining ground for the last few years.

According to a blog post (opens in new tab) by Microsoft's Matthias Troyer, one of the researchers involved in the study, this means that workloads such as drug design and protein folding, as well as weather and climate prediction would be better suited for classical systems after all, while chemistry and material science perfectly fit the bill for the "big compute, small data" philosophy.

While this may feel like an ice bucket challenge flop for the hopes of quantum computing, Troyer was quick to emphasize that that isn't the case: "If quantum computers only benefited chemistry and material science, that would be enough. Many problems facing the world today boil down to chemistry and material science problems," he said. "Better and more efficient electric vehicles rely on finding better battery chemistries. More effective and targeted cancer drugs rely on computational biochemistry."

But there's another element to the researchers thesis, one that's harder to ignore: it seems that current quantum computing algorithms would be insufficient, by themselves, to guarantee the desired "quantum advantage" result. Rather than the systems engineering complexity of a quantum computer, here it's a simple performance problem: quantum algorithms in general just don't provide enough of an acceleration. Grover's algorithm, for instance, offers a quadratic speedup over classical algorithms; but according to the researchers, that's not nearly enough.

"These considerations help with separating hype from practicality in the search for quantum applications and can guide algorithmic developments," the paper reads. "Our analysis shows it is necessary for the community to focus on super-quadratic speeds, ideally exponential speedups, and one needs to carefully consider I/O bottlenecks."

So, yes, it's still a long road toward quantum computing. Yet the IBMs and Microsofts of the world will steadily carry on their research to enable it. Many of the issues facing quantum computing today are the same we faced in developing classical hardware the CPUs, GPUs, and architectures of today just had a much earlier and more impactful start. But they still had to undergo the same design and performance iterations as quantum computing eventually will, within its own brave new timeframe. The fact that the paper was penned by scientists with Microsoft, Amazon Web Services (AWS) and the Scalable Parallel Computing Laboratory in Zurich all parties with vested interests into the development and success of quantum computing just makes that goal all the more likely.

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Single-GPU Systems Will Beat Quantum Computers for a While: Research - Tom's Hardware

How does quantum computing help the finance industry?

QCs are only in the developmental stage; experiments are already showing their great potential in the finance industry.

Based on the World Economic Forums estimate from 2022, national governments have invested more than $25 billion in quantum computing research, and over $1 billion in venture capital deals were closed in the previous year. Quantum computers (QCs) are in the early stages of development, and there are many technical challenges that need to be overcome before they can become practical tools for everyday use.

Nevertheless, they have already demonstrated great potential for applications in a wide range of fields. QCs have the ability to solve complex mathematical problems exponentially faster than classical computers, making them ideal for several complex tasks. The finance industry is one of the first runners in testing the technology. However, from the military to pharmaceuticals, logistics and manufacturing companies, several industries are experimenting with QC.

The mentioned features of QCs can have an enormous impact on the future of financial services. There are several tasks where financial forecasting and financial modeling can be supported by QCs for faster and more accurate processes. Notably, portfolio optimization, risk management and asset pricing are some of the most mentioned examples. However, their potential advantages and threats to cryptography make it important for financial service providers to monitor the technology.

Collaboration is crucial in the area of QCs due to the fact that technology and software development enable the revolution. Accelerating programs are initiated by the largest tech companies for experimentation with their hardware, software or cloud solutions, such as IBM, Microsoft, Google or Amazon.

Goldman Sachs has partnered with Microsoft Azure Quantum to explore the use of QCs for pricing. JPMorgan is experimenting with quantum solutions for optimization and risk management. HSBC announced its collaboration with IBM in 2022 to explore the use of QCs for pricing, portfolio optimization and risk mitigation.

QCs are new machines that can perform calculations much faster than classical computers, based on the principles of quantum mechanics.

The expression of QCs refers to a new type of machine based on the principles of quantum mechanics. Quantum mechanics is a division of physics that deals with the behavior of matter and light on the atomic and subatomic scales. The most valued property of QCs is that they perform certain types of calculations much faster than classical computers.

Classical computers store and process information in the unit of bits while QCs use quantum bits (or qubits). Bits represent information in a binary format and can have only two possible values: zero or one. Every piece of information going through a classical computer is essentially a long string of zeros and ones.

Qubits can exist in multiple states at once, a property known as superposition. This means that a single qubit can represent numerous possible combinations of zeros and ones; therefore, it can process a much larger amount of information than a classical bit.

Another exciting feature of qubits is the potential of entanglement, where qubit pairs are created. Modifying the state of one in the pair will change the state of the other qubit in a predictable way. This property gives extra power to QCs. Increasing the number of bits in a classical computer has a linear effect on the processing power, while adding an extra qubit to a quantum machine causes an exponential increase in the processing power.

Despite the great potential of QC, the technology and its applications need to overcome several challenging barriers.

Working with qubits is an enormously challenging scientific task because they need to be isolated in a controlled quantum state, which is extremely fragile. The smallest change in the physical environment (vibration or temperature) can cause an imbalance, which is the collapse of the superposition. Complex preventive actions are required, such as supercooled refrigerators, insulation or vacuum chambers to protect the system from losing its equilibrium.

Another aspect of the challenge is that as a different paradigm, QCs require not only completely new hardware and software but also algorithmic solutions. Numerous articles discuss the potential of QCs in machine learning, artificial intelligence or cryptography. Less often emphasized that it does not only mean using QCs to run algorithms designed for classical computers (quantum-enhanced) but building completely new algorithms, which are leveraging the features of QCs.

QCs in banking can be a game changer due to the potential of multiplying the speed and volume of calculations and transactions. However, different financial institutions only started to experiment with their own quantum algorithms and the limits of those potentials are not clear yet. Quantum algorithms are algorithms that take advantage of the unique properties of quantum systems, such as superposition and entanglement.

One example of quantum algorithms is Grovers algorithm, which can be used to search large, unstructured databases of financial data more quickly than classical algorithms. For example, it could be used to search for specific financial transactions or to identify patterns in financial data. Another example is Shors algorithm, which enables one to factor in large numbers more quickly than classical algorithms.

The finance industry is optimistic about quantum computing. Tasks such as portfolio optimization, risk management and asset pricing have a great chance to be beneficiaries.

Grovers and Shors algorithms can be applied to portfolio optimization. Portfolio optimization involves finding the optimal combination of investments to maximize returns while minimizing risk. Besides providing faster and more accurate calculations the technology can enable more flexible optimization strategies that take into account a wider range of factors, such as environmental, social and governance factors.

Another example could be asset pricing. Asset pricing is the process of estimating the value of financial assets such as stocks, bonds and derivatives. Traditional methods for pricing financial assets rely on complex mathematical models, such as Monte Carlo simulations, which involve simulating a large number of possible outcomes for a given financial asset and then using these simulations to estimate its value. Quantum Monte Carlo (QMC) can handle, for example, complex financial instruments, such as options, that have non-linear payoffs.

Heres the billion-dollar question: Can quantum computers predict the stock market? While QCs may have some advantages over classical computers in certain financial modeling tasks, it is unlikely that they will be able to predict the stock market with complete accuracy. Additionally, as with any new technology, quantum computing also poses its own unique challenges and limitations that need to be addressed before its full potential in financial applications can be realized.

Many financial services companies have high expectations of QCs effect on risk management. It involves identifying, assessing, prioritizing risks and taking actions to mitigate or manage those risks. Every step involves mathematical modeling and simulations for predicting risk outcomes, and time and accuracy play a crucial role in the process. Cybersecurity is an important part of risk management that can be enhanced by enabling more advanced encryption methods.

Encryption became a crucial measure in the banking industry that protects sensitive information from unauthorized access. It is used to secure communication channels between banking systems, websites and mobile apps and protect data on servers, databases and backups. Additionally, encryption is used to generate digital signatures that help ensure the authenticity of documents and prevent unauthorized modification or tampering of sensitive documents.

Cryptography and blockchain technology will surely not stay untouched by quantum computing; however, the direction remains a question.

Quantum computing presents both a threat and an opportunity for cryptography. While it has the potential to break many of the current encryption methods, it also has the potential to create new and more secure methods that are immune to attacks by classical computers.

QCs are exponentially faster than classical computers, which means they can quickly solve mathematical problems that classical computers would take years, decades or even centuries to solve. This includes the mathematical problems that underlie many of the encryption schemes used to secure digital communication and transactions.

For example, Shors algorithm can be used to efficiently factor large numbers, which is the basis for many public-key encryption algorithms such as RSA (the abbreviation refers to the name of the creators, RivestShamirAdleman).

However, quantum cryptography can also be used to create new cryptographic methods that are securer than classical methods. For example, quantum key distribution is a method to generate and distribute a secret key between two parties, the confidentiality and integrity of the information being exchanged can be ensured, even if a malicious entity intercepts the communication.

The mentioned features create some uncertainty in the future of QCs in blockchain technologies. It has the potential to break current encryption methods used in blockchain, which could compromise the security of digital assets and transactions. At the same time, researchers are working on developing quantum-resistant encryption methods for blockchains to counter this threat, such as CRYSTALS-Kyber public-key encryption by IBM. Additionally, QCs can enhance blockchains by increasing their processing speed and scalability, which can lead to more efficient and secure transactions.

Link:
How does quantum computing impact the finance industry? - Cointelegraph

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Large numbers can only be factorized with a great deal of computational effort. Physicists at the University of Innsbruck, Austria, led by Wolfgang Lechner are now providing a blueprint for a new type of quantum computer to solve the factorization problem, which is a cornerstone of modern cryptography. The research was recently published in Communications Physics.

Today's computers are based on microprocessors that execute so-called gates. A gate can, for example, be an AND operation, i.e., an operation that adds two bits. These gates, and thus computers, are irreversible. That is, algorithms cannot simply run backwards. "If you take the multiplication 2x2=4, you cannot simply run this operation in reverse, because 4 could be 2x2, but likewise 1x4 or 4x1," explains Wolfgang Lechner, professor of theoretical physics at the University of Innsbruck. If this were possible, however, it would be feasible to factorize large numbers, i.e., divide them into their factors.

Martin Lanthaler, Ben Niehoff and Wolfgang Lechner from the Institut fr Theoretische Physik at the University of Innsbruck and the quantum spin-off ParityQC have now developed exactly this inversion of algorithms with the help of quantum computers. The starting point is a classical logic circuit, which multiplies two numbers. If two integers are entered as the input value, the circuit returns their product. Such a circuit is built from irreversible operations. "However, the logic of the circuit can be encoded within ground states of a quantum system," explains Martin Lanthaler from Wolfgang Lechner's team. "Thus, both multiplication and factorization can be understood as ground-state problems and solved using quantum optimization methods."

"The core of our work is the encoding of the basic building blocks of the multiplier circuit, specifically AND gates, half and full adders with the parity architecture as the ground state problem on an ensemble of interacting spins," says Martin Lanthaler.

The coding allows the entire circuit to be built from repeating subsystems that can be arranged on a two-dimensional grid. By stringing several of these subsystems together, larger problem instances can be realized. Instead of the classical brute force method, where all possible factors are tested, quantum methods can speed up the search process: To find the ground state, and thus solve an optimization problem, it is not necessary to search the whole energy landscape, but deeper valleys can be reached by "tunneling."

The current research work provides a blueprint for a new type of quantum computer to solve the factorization problem, a cornerstone of modern cryptography. This blueprint is based on the parity architecture developed at the University of Innsbruck and can be implemented on all current quantum computing platforms.

More information: Martin Lanthaler et al, Scalable set of reversible parity gates for integer factorization, Communications Physics (2023). DOI: 10.1038/s42005-023-01191-3

Journal information: Communications Physics

Read more from the original source:
A blueprint for a quantum computer in reverse gear - Phys.org

May 5, 2023• Physics 16, 75

A new kind of 3D optical lattice traps atoms using focused laser spots replicated in multiple planes and could eventually serve as a quantum computing platform.

Researchers have produced 3D lattices of trapped atoms for possible quantum computing tasks, but the standard technology doesnt allow much control over atom spacing. Now a team has created a new type of 3D lattice by combining optical tweezerspoints of focused light that trap atomswith an optical phenomenon known as the Talbot effect [1]. The teams 3D tweezer lattice has sites for 10,000 atoms, but with some straightforward modifications, the system could reach 100,000 atoms. Such a large atom arrangement could eventually serve as a platform for a quantum computer with error correction.

3D optical lattices have been around for decades. The standard method for creating them involves crossing six laser beams to generate a 3D interference pattern that traps atoms in either the high- or low-intensity spots (see Synopsis: Pinpointing Qubits in a 3D Lattice). These cold-atom systems have been used as precision clocks and as models of condensed-matter systems. However, the spacing between atoms is fixed by the wavelength of the light, which can limit the control researchers have over the atomic behavior.

Optical tweezers offer an alternative method for trapping and controlling atoms. To form a tweezer array, researchers pass a single laser beam through a microlens array (or similar device) that focuses the beam into a 2D pattern of multiple bright spots. Atoms are automatically drawn to the centers of these spots, forming an array in a single plane (see Viewpoint: Alkaline Atoms Held with Optical Tweezers). We take these tweezer arrays to the third dimension, says Malte Schlosser from the Technical University of Darmstadt, Germany.

To obtain a 3D lattice, Schlosser and his colleagues took advantage of the Talbot effect, which is an interference phenomenon that occurs when light strikes a periodic structure, such as a diffraction grating or a microlens array. The light exiting the structure produces a 2D interference pattern of bright spots at some fixed distance beyond the structure but also generates additional planes of spots parallel to the first one. The Talbot effect had long been considered a nuisance for tweezer array research, as it creates extra bright spots that trap stray atoms, which interferes with measurements. The researchers turned this bug into a feature by deliberately tuning their optical system to trap atoms in the extra bright spots, Schlosser explains.

The researchers shined an 800-milliwatt laser onto a microlens array, which produced a 2D square array of 777 atom traps at the focal plane of the lens. But thanks to the Talbot effect, this 2D array was reproduced in 17 parallel planes, giving a total of 10,000 atom traps. These Talbot planes come for free, so we dont have to put in additional laser power or additional laser beams, Schlosser says.

As a demonstration of their system, Schlosser and his colleagues showed that they could load around 50% of the traps with rubidium atoms and induce an optical transition in all the atoms in a sublattice. In the future, the team plans to use a focused laser beam to selectively excite a single atom. Such optical control could allow researchers to read the atoms state or to place it in a so-called Rydberg state that would let it interact with its neighbors. Control of atomatom interactions has been previously demonstrated in 2D tweezer arrays. Schlosser foresees having atomatom interactions in the 3D lattice, but currently the spacing between the planes is too large (around 100 m); a distance of 10 m or less would be required.

Besides squeezing down the spacing of the lattice, the team plans to explore other trap geometries, such as hexagonal patterns that could mimic materials like graphene. The researchers are also working to boost the laser power. More light will increase the number of traps in the lattice. They estimate that doubling the power would provide 30,000 traps and that quadrupling it should produce close to 100,000.

Schlosser and his colleagues are tackling one of the most important challenges any quantum computing technology will face, which is scaling, says Ben Bloom, founder and chief technology officer of Atom Computing, a quantum technology company in California. He says that the new design can create a large number of atom quantum bits at essentially no cost, but there will be challenges ahead in trying to control the atoms within the lattice. Still, controlling so many atoms will have practical benefits. Pushing to large numbers of individually controlled atoms in 3D will allow for the exploration of new quantum error-correction codes, Bloom says.

Michael Schirber

Michael Schirber is a Corresponding Editor forPhysics Magazine based in Lyon, France.

A new experiment follows the trajectories of electrons as pulsed laser light yanks them away from their atoms and slams them back. Read More

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Physics - Tweezers in Three Dimensions - Physics

LONDON, May 2, 2023 /PRNewswire/ -- Omdia forecasts that quantum computing vendors will see their global revenue rise from $942 million in 2022 to $22 billion in 2032, for a compound annual growth rate of 57.7% over this ten-year period. North America and Europe are expected to be the leading regional markets, with Asia & Oceania a close third. Cloud-based access services will make up the largest share of revenue, followed by hardware, consulting, and software. Omdia also believes 2027 will be a key inflection point in the market, and that the chances of a "quantum winter" are very small (less than 1%).

Near term, Omdia believes examples of "quantum commercial advantage" in which a quantum computer supplies a measurable advantage in speed, cost, quality, or efficiency over the typical classical alternative for a problem of commercial interest will grow steadily. By 2027, enough of these examples will be clear across enough verticals and industries that adopters will shift from "experimenting with quantum computing" to "deploying quantum computing for operational needs."

"Achieving fault-tolerant scaled quantum computing would help humanity solve key challenges related to climate change, developing new pharmaceuticals and materials, and bringing important advances to artificial intelligence." says Sam Lucero, Chief Analyst for Quantum Computing at Omdia. "But the industry has a long road to fully achieving this goal."

Recently, concerns have grown about the possibility of a "quantum winter". However, Omdia notes several factors protecting against such a downturn, including growing investments in vendors, strong government support, diverse technology options, and steady technology advancements by vendors.

"While a 'quantum winter' is possible," says Lucero "the chance of it happening is far outweighed by the likelihood of continued steady progress towards fault-tolerant, scaled quantum computers."

Omdia published its annual quantum computing market forecast report in April 2023.

ABOUT OMDIA

Omdia, part of Informa Tech, is a technology research and advisory group. Our deep knowledge of tech markets combined with our actionable insights empower organizations to make smart growth decisions.

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Omdia forecasts quantum computing market will grow more than 22x ... - PR Newswire