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In a moment of triumph thats being hailed as equivalent to the move from room-scale silicon technology down to desk-sized machines, quantum computing has now gone chip-scale down from the room-scale contraptions you might have seen elsewhere, including in science fiction.

The development has been spearheaded by Cambridge-based quantum specialist Riverlanes work with New York and London-based digital quantum company Seeqc. Theyre the first to deploy a quantum computing chip that has an integrated operating system for workflow and qubit management (qubits are comparable to classical computings transistors, but capable of pairing between themselves, instantly sharing information via quantum states, and also capable of representing both a 0 and a 1). The last time we achieved this level of miniaturization on a computing technology, we started the computing revolution. Now, expectations for a quantum revolution are on the table as well, and the world will have to adapt to the new reality.

The new chip ushers in scalable quantum computing, and the companies hope to scale the design by increasing surface area and qubit count. The aim is to bring qubits up to millions, a far cry from their current deployed maximum of a (comparatively puny, yet still remarkably complex) 76-qubit system that enabled China to claim quantum supremacy. There are, of course, other ways to scale besides increased qubit counts. Deployment of multiple chips in a single self-contained system or through multiple, inter-connectable systems could provide easier paths to quantum coherency. And on that end, a quantum OS is paramount. Enter Deltaflow.OS.

Deltaflow.OS is a hardware and platform-agnostic OS (think Linux, which populates everything from smartphones to IoT to supercomputers), meaning that it can serve as the control mechanism for various quantum deployment technologies currently being pursued around the globe. And even as multiple independent companies such as Google, Microsoft, and IBM, to name a few pursue the holy grail of quantum supremacy, Riverlanes Deltaflow.OS is an open-source, Github-available OS that's taking the open approach for market penetration.

And this makes sense, since the more than 50 quantum computers already built around the world all operate on independently-developed software. Its such a nascent field still that there are no standards regarding the deployment and control systems. An easily-deployable, quantum hardware-agnostic OS will undoubtedly accelerate development of applications that take advantage of quantum computings strengths, which at the 76 qubit system of China, already enables certain workloads to be crunched millions of times faster than the fastest classical, Turing-type supercomputer could ever hope to achieve.

To achieve this, Riverlane has effectively created a layered Digital Quantum Managament (DQM) SoC (System-On-Chip) that pairs classical computing capabilities with quantum mechanics. The companys diagrams demonstrate what it calls an SFQ (Single Flux Quantum) co-processor as the base layer of the design, which enables the OS to be exposed to developers with a relatively familiar interface for interaction with the qubits. This offers the capability to perform digital qubit control, readout and classical data processing functions, as well as being a platform for error correction.

There are numerous advantages to be taken from this approach, as the SFQs resources are (...) proximally co-located and integrated with qubit chips in a cryo-cooled environment to drastically reduce the complexity of input/output connections and maximize the benefits of fast, precise, low-noise digital control and readout, and energy-efficient classical co-processing. Essentially, some tenets of classical computing still apply, in that the closer the processing parts are, the more performant they are. This enables the OS to run, and is layered next to an active qubit sheet that actually performs the calculations.

Quantum computing has long been the holy grail in development for new processing technologies. However, the complexity of this endeavour cant be understated. The physics for quantum computing are essentially being written as we go, and while that is true, in a way, for many technological and innovation efforts, nowhere does It happen as much as here.

There are multiple questions related to quantum computing and its relationship to classical computing. Thanks to the efforts of Riverlane and Seeqc, the quantum computing ecosystem can now align their needles and collectively problem-solve for deployment and operation of quantum-computing-on-a-chip solutions.

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Quantum Computing on a Chip: Brace for the Revolution - Tom's Hardware

Delft, Netherlands -- July 15, 2021 -- Today Dutch startup QuantWare has launched the worlds first commercially available superconducting processor for quantum computers (QPU). This is the first time superconducting quantum processors have been available off the shelf, a development with the potential to significantly accelerate the quantum computing revolution.

Quantum technology promises to significantly expand the amount of data computers are able to process, which could have huge implications for fields such as A.I., medicine, business intelligence, and cybersecurity. But the quantum industry is still young and scaling is difficult. Companies building parts for quantum computers need qubits, the microscopic objects that make quantum computing possible, but it is often cost prohibitive for them to produce them themselves. QuantWares superconducting QPUs eliminate that barrier and may be instrumental in accelerating the development of the quantum computer market.

Superconducting is the leading and most mature approach to quantum processors - Google achieved quantum supremacy in 2019 using superconducting QPUs. While other QPUs are already available off the shelf, this is the first time a superconducting QPU has been easily available in productised form, leveling the playing field for quantum experimentation.

QuantWares proprietary product, Soprano, is a 5-qubit QPU. In an article published by Ars Technica, QuantWare shared that the fidelities of each qubit will be 99.9 percent, which should keep the error rate manageable. 5 qubits is sufficient for the immediate customer base QuantWare expects to attract, namely research institutions and university labs.

The race towards useful Quantum Computation is heating up, but still reserved to a small group of companies. By making QPUs more available, we will speed up the development of practical quantum-driven solutions to the worlds biggest problems. said QuantWare co-founder Dr. Alessandro Bruno.

Another way to achieve Quantum Advantage is by designing a chip specifically for a particular application. The startup wants to exploit this by making co-designed QPUs together with software companies to allow them to develop processors specialized in their algorithms.

QuantWare was founded in 2020 by quantum engineer Dr. Alessandro Bruno and Delft University of Technology (TU Delft) graduate MSc Matthijs Rijlaarsdam. They met while doing research at QuTech, a quantum technology research institute at TU Delft in the Netherlands. The company recently closed their pre-seed funding round, meaning the company has now raised 1.15M. They plan to quickly expand their team and upgrade their processors towards higher qubit numbers. One of their growth goals for the rest of the year is to expand fabrication capabilities and partnerships - QuantWare hopes to become a collaborative bridge between quantum companies worldwide. The company is already looking for new operational facilities, as they expect to outgrow their current building within months. QuantWares first two products, Crescendo and Soprano, are now available for pre-order.

Investors

About QuantWare

QuantWare builds super-conducting quantum processors and related hardware. The processors lie at the heart of quantum computers and are crucial for conducting research in this field. By providing processors, QuantWare is making quantum research accessible to researchers and startups. The company also develops technology that will increase the computational power of processors beyond current restrictions. QuantWares innovations are creating a new standard for quantum processors.

About UNIIQ

UNIIQ is a 22 million investment fund focused on the proof-of-concept phase, which helps entrepreneurs in West Holland bring their unique innovation to market faster. UNIIQ offers entrepreneurs the seed capital to achieve their plans and bridge the riskiest phase from concept to promising business. A consortium, including Erasmus MC, TU Delft, Leiden University and the regional development agency InnovationQuarter, created the fund. In 2021, Erasmus University Rotterdam also joined the fund. UNIIQ is made possible by the European Union, the Province of South Holland and the municipalities of Rotterdam, The Hague and Leiden. InnovationQuarter is responsible for the fund management.

About FORWARD.one

FORWARD.one is a VC fund focussed on investing in high-tech start-ups and scale-ups. With a team of financial professionals and technology entrepreneurs, FORWARD.one actively supports its portfolio companies to achieve their goals and ambitions. After successfully deploying the first fund in 11 promising start-ups, FORWARD.one has recently launched its second fund with a size of 100m. With this fund FORWARD.one will continue to invest in ambitious high-tech entrepreneurs and their companies.https://www.forward.one/

About Rabobank Startup & Scale-up Team

Start-ups and scale-ups are the innovators of the economy, contributing significantly to solving societal challenges, and are the main engine for economic growth and employment in the Netherlands. This target group therefore represents great commercial and strategic value for Rabobank. The Startup & Scale-up Team helps entrepreneurs who share this mission to grow sustainably by opening up their (international) network, by providing knowledge and funding.

Original post:
Quantware Launches the World's First Commercially Available Superconducting Quantum Processors, Accelerating the Advent of the Quantum Computer. -...

image: Dolev Bluvstein (from left), Mikhail Lukin, and Sepehr Ebadi have developed a special type of quantum computer known as a programmable quantum simulator. Evadi is adjusting the devices that make them possible to see More

Credits: Rose Lincoln / Harvard Staff Photographer

A team of physicists at the Harvard MIT Ultra-Cryogenic Atomic Center and other universities have developed a special type of quantum computer known as a programmable quantum simulator that can operate at 256 qubits or qubits.

The system sheds light on the host of complex quantum processes, ultimately helping to bring real-world breakthroughs in materials science, communications technology, finance, and many other areas. It shows a big step towards building. Overcome research hurdles beyond the capabilities of todays fastest supercomputers. Qubits are the basic building blocks of quantum computers and are the source of their enormous processing power.

This moves the field to a new territory that no one has ever been to, said Mikhail Lukin, a professor of physics at George Vasmer Leverett, co-director of the Harvard Quantum Initiative and one of the senior authors of the study. Stated.Published in the journal today Nature.. We are entering a whole new part of the quantum world.

According to Sepehr Ebadi, a physics student at the Graduate School of Arts and Sciences at Harvard and the lead author of the study, the unprecedented combination of size and programmability of the system is at the forefront of the quantum computer competition. The mysterious nature of the substance on a very small scale greatly improves its processing power. Under the right circumstances, increasing the cue bit means that the system can store and process more information exponentially than the traditional bits on which a standard computer runs.

The number of quantum states possible with just 256 qubits exceeds the number of atoms in the solar system, Evadi explained the vast size of the system.

Already, the simulator allows researchers to observe some exotic quantum states that have never been experimentally realized, and is accurate enough to serve as an example in a textbook showing how magnetism works at the quantum level. Quantum phase transition research can be performed.

These experiments provide powerful insights into the quantum physics that underlie material properties and help scientists show how to design new materials with exotic properties.

The project uses a significantly upgraded version of the platform developed by researchers in 2017 that was able to reach a size of 51 qubits. The old system allowed researchers to capture ultra-low temperature rubidium atoms and place them in a particular order using a one-dimensional array of individually focused laser beams called optical tweezers.

This new system allows atoms to be assembled into a two-dimensional array of optical tweezers. This increases the achievable system size from 51 qubits to 256 qubits. Tweezers allow researchers to arrange atoms in a defect-free pattern and create programmable shapes such as squares, honeycombs, or triangular grids to design different interactions between cubits.

The flagship product of this new platform is a device called the Spatial Light Modulator, which is used to form the light wave front and generate hundreds of individually focused optical tweezers beams, Ebadi said. Mr. says. These devices are essentially the same as those used in computer projectors to display images on the screen, but we have adapted them as an important component of quantum simulators.

The initial loading of atoms into optical tweezers is random, and researchers need to move the atoms to place them in the shape of the target. Researchers use a second set of moving optical tweezers to drag the atom to the desired position, eliminating the initial randomness. Lasers give researchers complete control over the placement of atomic cubits and their coherent quantum manipulation.

Other senior authors of this study include Professors Svil Sachidef and Marcus Greiner of Harvard University, Stanford University, University of California Berkeley, and Insbrook University of Austria, who worked on the project with Professor Vladin Vretti of Massachusetts Institute of Technology. Includes scientists. Austrian Academy of Sciences and QuEra Computing Inc. in Boston.

Our work is part of a very fierce, highly visible global competition to build larger, better quantum computers, said Harvard University Physics Researcher. Tout Wang, one of the authors of the paper, said. Overall effort [beyond our own] There are leading academic research institutes involved and major private sector investments from Google, IBM, Amazon, and many others.

Researchers are currently working on improving the system by improving laser control over qubits and making the system more programmable. They are also actively exploring how systems can be used in new applications, from exploring the exotic forms of quantum materials to solving challenging real-world problems that can be naturally encoded into qubits. doing.

This study enables a huge number of new scientific directions, Evadi said. We are far from the limits of what we can do with these systems.

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Harvard-led physicists have taken a major step in the competition with quantum computing

Source link Harvard-led physicists have taken a major step in the competition with quantum computing

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Harvard-led physicists have taken a major step in the competition with quantum computing - Illinoisnewstoday.com

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