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Among the most promising applications of quantum computing, quantum machine learning is set to form waves. But how this could be achieved is still a bit of a mystery.

IBM researchers now claim to have mathematically proven it With a quantum approach, some machine learning problems can be solved faster than conventional computers.

Machine learning is a well-established branch of artificial intelligence, and it is already used in many industries to solve different problems. This involves training an algorithm with large data sets, in order to allow the model to identify different patterns and ultimately calculate the best answer when new information is provided.

With larger data sets, a machine learning algorithm can be improved to provide more accurate answers, but this comes at a computational cost that quickly reaches the limits of traditional hardware. Thats why researchers hope that one day they will be able to harness the enormous computing power of quantum techniques to take machine learning models to the next level.

One method in particular, called quantum nuclei, is the subject of many research articles. In this approach, a quantum computer intervenes only for part of the global algorithm, by expanding the so-called characteristic space, that is, the set of properties used to characterize the data submitted to the model, such as gender or age if the system is trained to recognize patterns in people.

To put it simply, using a quantum nucleus approach, a quantum computer can distinguish between a larger number of features and thus identify patterns even in a huge database, whereas a classical computer would not see just random noise.

IBM researchers set out to use this approach to solve a specific type of machine learning problem called classification. As the IBM team explains, the most common example of a classification problem is a computer that receives pictures of dogs and cats and needs to be trained with this data set. The ultimate goal is to allow it to automatically tag all future images it receives whether it is a dog or a cat, with the goal of creating accurate tags in the least amount of time.

Big Blue scientists developed a new classification task and found that a quantum algorithm using the quantum kernel method was able to find relevant features in the data for accurate labeling, while for classical computers, the data set looked like random noise.

The routine we are using is a general method that in principle can be applied to a wide range of problems, Kristan Temme, a researcher at IBM Quantum, told ZDNet. In our research paper, we formally demonstrated that a quantum kernel estimation routine can lead to learning algorithms that, for specific problems, go beyond classical machine learning approaches.

To demonstrate the advantage of the quantum method over the classical approach, the researchers created a classification problem for which data could be generated on a classical computer, and showed that no classical algorithm could do better than a stochastic response to answer the problem.

However, when they visualized the data in a quantum feature map, the quantum algorithm was able to predict the labels very accurately and quickly.

The research team concludes, This article can be considered an important step in the field of quantum machine learning, as it demonstrates a comprehensive acceleration of a quantum nucleus method implemented in a fault-tolerant manner with realistic assumptions.

Of course, the classification task developed by scientists at IBM is specifically designed to determine whether the quantum nucleus method is useful, and is still far from ready to apply to any kind of large-scale business problem.

According to Kristan Temme, this is mainly due to the limited size of IBMs current quantum computers, which so far can only support less than 100 qubits. There are far from the thousands, if not millions, of qubits that scientists believe are necessary to start creating value in the field of quantum technologies.

At this point, we cant cite a specific use case and say this will have a direct impact, the researcher adds. We have not yet realized the implementation of a large quantum machine learning algorithm. The size of this algorithm is of course directly related to the development of quantum matter.

IBMs latest experiment also applies to a specific type of classification problem in machine learning, and it does not mean that all machine learning problems will benefit from the use of quantum cores.

But the results open the door to further research in this area, to see if other machine learning problems could benefit from using this method.

So much work is still up for debate at the moment, and the IBM team has recognized that any new discovery in this area has many caveats. But until quantum hardware improves, researchers are committed to continuing to prove the value of quantum algorithms, even if from a mathematical point of view.

Source : ZDNet.com

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IBM shows the advantages of a quantum computer over traditional computers - Tech News Inc

Quantum computing: this is how quantum programming works using the example of random walk

The quantum mistake

Dont look after every coin toss

Conclusion

Read article in iX 13/2021

Developers are familiar with software development on classic computers. Intuitive programming languages, which are based on familiar thought and language patterns, enable even newbies to get started quickly and achieve initial success with small applications.

When programming a quantum computer, the situation is more complicated and significantly more abstract due to the underlying laws of quantum mechanics. The differences between programming on a classical and a quantum computer should be illustrated by an example.

Steffen is going on vacation. Immediately he was drawn to the beach promenade. At five oclock in the morning he stumbled out of a bar, heavily drunk, and couldnt remember which way his hotel was facing. But he has to get there as soon as possible if he wants to reserve a lounger in the first row by the hotel pool at 6:00 a.m. Steffen thinks about it: The hotel must be somewhere on this street. In a math lecture several years ago, the professor had said something about random walks and that the walker can reach any point on a line after any number of steps.

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Disclaimer: This article is generated from the feed and not edited by our team.

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Quantum computing: this is how quantum programming works using the example of random walk - Market Research Telecast

The company's 100-qubit gate-based quantum computer, code-named Hilbert, is launching later this year after final tuning and optimization work.

By cooling atoms down to near absolute zero and then controlling them with lasers, a company has successfully created a 100-qubit quantum processor that compares to the systems developed by leading quantum players to date.

ColdQuanta, a US-based company that specializes in the manipulation of cold atoms, unveiled the new quantum processor unit, which will form the basis of the company's 100-qubit gate-based quantum computer, code-named Hilbert, launching later this year after final tuning and optimization work.

There are various different approaches to quantum computing, and among those that have risen to prominence in the last few years featuresuperconducting systems,trapped ions,photonic quantum computersand evensilicon spin qubits.

SEE: Building the bionic brain (free PDF) (TechRepublic)

Cold atoms, on the other hand, haven't made waves in the quantum ecosystem so far. ColdQuanta's 100-qubit quantum processor, however, could seemingly compete against the industry's highest standards: for example, IBM's current quantum system, Hummingbird, supports 65 qubits.

And in the next three years, ColdQuanta is hoping to create a system surpassing 1,000 qubits. This again aligns with IBM's roadmap for quantum hardware,which should see the company releasing a 1,121-qubit quantum computer in 2023.

"We hear a lot about superconducting and trapped ions and in some respects cold atom is the new kid on the block, but we believe it has great promise in terms of scalability," Paul Lipman, president of quantum computing at ColdQuanta, tells ZDNet.

ColdQuanta's approach consists of treating atoms like qubits, and bringing them down to extremely cold temperatures, where their quantum properties can be manipulated with great precision. This is because, in such an isolated environment, atoms are protected from environmental noise and can retain their quantum properties for much longer.

Cooling down particles to exert better control over them is not new to the quantum world: Google and IBM's superconducting processors also require placing qubits in huge dilution refrigerators, where temperatures are brought down to zero kelvin (-273.15C).

But ColdQuanta's cold atoms approach goes one step further. Atoms are cooled down to the microkelvin level that is, a thousand times colder than in the superconducting method.

Rather than using large refrigerators, however, ColdQuanta traps the atoms with lasers to cool them down, before using a combination of lasers and microwave pulses to arrange them into the gates that make up a quantum circuit.

"Because we cool them down with lasers rather than dilution refrigerators, we don't have the same scaling challenges in terms of building enormous fridges that can hold large numbers of qubits," says Lipman. "We cool them down to microkelvin, but we do that in a device that can fit in your hand at room temperature."

What's more: atoms are ten-thousand times smaller than superconducting qubits, according to Lipman, meaning that many cold atom qubits can be packed closely together on a much smaller space. What would require square-meters worth of space for a superconducting quantum processor can sit on a cold atom system the size of a nail, according to the company.

"Cold atoms have this intrinsic scalability that is very attractive," argues Lipman.

Cold atoms' ability to scale rapidly is one of ColdQuanta's key selling points, but there remain some engineering challenges that, for now, still limit Hilbert's size. The company's scientists are looking at how the use of lasers changes when the qubit count increases by orders of magnitude, for instance, and testbeds are already underway in the lab to determine the best path forward.

The fundamental principles of the approach, however, are tested and proven, says Lipman, and cold atoms already perform similarly to leading-edge quantum processors. Not only on qubit count: the company's data also shows thatthe system is comparable to IBM and Google's quantum computerswhen it comes to connectivity, which refers to the number of qubits that can interact with one another, and coherence, which is the duration of time that quantum properties can be maintained.

On fidelity, however, the processor lags slightly behind the devices developed by competitors, meaning that the accuracy of ColdQuanta's system isn't as high. But part of the optimization work going on now, says Lipman, is dedicated to boosting Hilbert's performance on fidelity.

Lipman is confident that these promising results will set ColdQuanta apart in an ecosystem that is growing at pace. New milestones are announced by quantum companies large and small at a rapid pace, and the number of approaches to quantum computing is multiplying fast, each with their own benefits and challenges making it increasingly difficult to distinguish hype from reality.

"It's too early to tell which modality will win the race," admits Lipman. "If you roll the clock forward two or three years, there might even be modalities that we don't even have publicly available information on today, but may come to the forefront."

"We'll learn more once the computer is released, but our focus now is to work with potential customers to deliver tangible near-term value."

ColdQuanta has not publicly announced any customers yet, but the company is working particularly on optimization problems, which could find applications in logistics, material science and telecommunications.

The firm also has a long-standing partnership with the Defense Advanced Research Projects Agency (DARPA), which awarded ColdQuanta a total $7.4 million to develop a scalable cold-atom-based quantum computer for defense applications such as resource allocation, logistics, and image recognition.

Hilbert is expected to launch later this year and will be available over ColdQuanta's private cloud. The company is also in talks with Amazon, Microsoft and Google to eventually make the quantum computer accessible over AWS, Azure and Google Cloud.

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Quantum computing: This new 100-qubit processor is built with atoms cooled down near to absolute zero - ZDNet

Ilana Wisby is the CEO of Oxford Quantum Computing, a spin-out from the University of Oxford in the UK.

A startup in the UK is now offering cloud-based access to its own superconducting quantum computer but with a twist that it hopes could one day help it compete against the processors developed by quantum giants such as IBM and Google.

Oxford Quantum Circuits (OQC), a startup that spun out of the University of Oxford, is approaching superconducting quantum computing slightly differently. Leading superconducting quantum systems are typically built in a two-dimensional plane, with each qubit acting like a unit cell that requires intricate wiring for controls and measurements. Increasing the number of qubits means increasing the amount of wiring and on a 2D plane, this comes with a higher risk of creating environmental noise that can damage the quality of the system.

Instead, OQC's researchers use a three-dimensional architecture that moves the control and measurement wiring out of plane. With key componentry off-chip, says OQC, the superconducting quantum processor is a more flexible and engineerable system.

SEE: Building the bionic brain (free PDF) (TechRepublic)

Dubbed the "Coaxmon," this new design approach ultimately has the potential to make it is easier to scale up the number of qubits on the processor without losing coherence, the company said.

"The Coaxmon was designed from principle to meet some of the underlying scaling challenges with superconducting technologies," Ilana Wisby, the CEO of OQC, tells ZDNet. "We've taken all of that wiring which is a really big element to reducing the power of what we can do with a processor off the chip, meaning that the Coaxmon is inherently a lot more scalable."

According to Wisby, the 3D architecture means that it is possible to increase the qubit count on the processor without resorting to complex fabrication steps for extra wiring, and without running the risk of reducing the system's coherence.

Despite the promising pitch, the quantum computer that OQC has just brought online, called Sophia, is only four qubits strong. In comparison, IBM's current quantum processor can support 65 qubits, and the company is working towards launching a 127-qubit system by the end of the year.

Even then, IBM's quantum computer won't be bringing any significant business value for users: quantum technologies are not expected to start showing any real-world usefulness until they are capable of supporting at least 1,000 qubits. In that light, OQC's new quantum computer still seems to have some way to go before it can compete against the services offered by some of the largest corporations dominating the quantum ecosystem.

But Wisby explains that this is just the start. As a University of Oxford spinout, she says, OQC has until recently mostly developed in the context of university labs, where cost efficiency was key and minds were focused on proving the fundamentals of the technology.

In the last year, however, OQC built and opened its own quantum lab, a facility fitted with all of the cryogenic equipment, cleanrooms, power and data supplies, ducted fume cupboards and other exotic quantum essentials that are necessary to building up a quantum system.

Sophia's low qubit count is, therefore, a business problem rather than a technology one, argues Wisby. "But setting up our own independent commercial lab has marked a moment of independence for the company," she says.

"It's only really now that we've changed our company goals to proving the business model, which obviously has more focus on scaling the full system."

The long-term goal, she assures, is to build a universal, fault-tolerant quantum computer an objective that aligns with that of the largest tech giants currently developing quantum technologies.

Of course, there remain many obstacles to scaling. While increasing the number of qubits in the processor is a challenge in itself, it is also key to ensure that the overall system's support infrastructure and architecture can grow in parallel. OQC, therefore, has secured partnerships with companies like Oxford Instruments to start thinking about the future iterations of Sophia.

For now, OQC is focusing on attracting customers to its brand-new cloud service, which it has just launched to provide customers with access to Sophia via a private cloud.

OQC has now invited businesses to join the company's beta list, to test how they could experiment with new quantum approaches. With only four qubits, however, the scope of potential applications will remain very limited.

Among those already signed up, fellow UK-based quantum computing company Cambridge Quantum is already planning to test Sophia with its IronBridge platform a cybersecurity service that leverages the unpredictability of quantum computers to generate un-hackable cryptographic keys.

Wisby also points to a long-standing partnership with software company Riverlane, which has already been using OQC's quantum computer to run a chemical simulation algorithm names alpha-VQE.

Riverlane and OQC have also been working together todevelop a quantum operating system, Deltaflow.OS,which would allow the same quantum software to run on different types of quantum computing hardware.

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This quantum computer with a 3D chip is heading into the cloud - ZDNet

LONDON and CAMBRIDGE, England, July 13, 2021 Rigetti UK announced today it will partner with Riverlane and Astex Pharmaceuticals to develop an integrated application for simulating molecular systems using Rigetti Quantum Cloud Services, paving the way for a commercial application that could transform drug discovery in pharmaceutical R&D.

Our consortium brings together a complete quantum supply chain from hardware to end-user allowing us to develop a tailor-made solution to address a problem of real value to the pharmaceutical sector, says Mandy Birch, SVP of Technology Partnerships at Rigetti. This project lays the groundwork for the commercial application of Rigetti Quantum Cloud Services in the pharmaceutical industry.

The average cost of discovering a new drug and bringing it to market has tripled since 2010, reaching almost $3bn in 2018. However, soaring R&D costs have not translated into shorter times to market or higher numbers of newly approved drugs.

We want to solve this problem by using quantum computers to speed up the process of drug discovery, says Chris Murray, SVP Discovery Technology at Astex. Quantum computers provide a fundamentally different approach that could enable pharmaceutical companies to identify, screen, and simulate new drugs rather than using expensive, trial-and-error approaches in the laboratory.

To design more efficient drugs and shorten the time to market, researchers rely on advanced computational methods to model molecular structures and the interactions with their targets. While classical computers are limited to modelling simple structures, quantum computers have the potential to model more complex systems that could drastically improve the drug discovery process. However, todays quantum computers remain too noisy for results to evolve past proof-of-concept studies.

Building on previous work with Astex, our collaboration aims to overcome this technological barrier and address a real business need for the pharmaceutical sector, says Riverlane CEO Steve Brierley. The project will leverage Riverlanes algorithm expertise and existing technology for high-speed, low-latency processing on quantum computers using Rigettis commercially available quantum systems. The team will also develop error mitigation software to help optimise the performance of the hardware architecture, which they expect to result in up to a threefold reduction in errors and runtime improvements of up to 40x. This is an important first step in improving the performance of quantum computers so that they can solve commercially relevant problems, Brierley adds.

Science Minister Amanda Solloway says, The UK has bold ambitions to be the worlds first quantum-ready economy, harnessing the transformative capabilities of the technology to tackle global challenges such as climate change and disease outbreak.

This government-backed partnership will explore how the power of quantum could help boost drug discovery, with the aim of shortening the time it takes potentially life-saving drugs to transfer from lab to market, all while cementing the UKs status as a science superpower.

The 18-month feasibility study is facilitated by a grant through the Quantum Challenge at UK Research and Innovation (UKRI). Rigetti UK has previously received funding from UKRI to develop the first commercially available quantum computer in the UK. Riverlane has also received funding from UKRI to develop an operating system that makes quantum software portable across qubit technologies.

About Rigetti UK

Rigetti UK Limited is a wholly owned subsidiary of Rigetti Computing, based in Berkeley, California. Rigetti builds superconducting quantum computing systems and delivers access to them over the cloud. These systems are optimized for integration with existing computing infrastructure and tailored to support the development of practical software and applications. Learn more at rigetti.com.

About Riverlane

Riverlane builds ground-breaking software to unleash the power of quantum computers. Backed by leading venture-capital funds and the University of Cambridge, it develops software that transforms quantum computers from experimental technology into commercial products. Learn more at riverlane.com.

About Astex

Astex is a leader in innovative drug discovery and development, committed to the fight against cancer and diseases of the central nervous system. Astex is developing a proprietary pipeline of novel therapies and has a number of partnered products being developed under collaborations with leading pharmaceutical companies. Astex is a wholly owned subsidiary of Otsuka Pharmaceutical Co. Ltd., based in Tokyo, Japan.

For more information about Astex Pharmaceuticals, please visit https://astx.comFor more information about Otsuka Pharmaceutical, please visit http://www.otsuka.co.jp/en/

Source: Rigetti UK

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Rigetti Computing Partners with Riverlane, Astex Pharmaceuticals on Quantum Computing for Drug Discovery - HPCwire