The compact quantum computer fits into two 19-inch server racks.

Quantum computers still require large, dedicated rooms and complex installations, but now, in a new step towards bringing the technology out of the lab, researchers have designed a prototype quantum computer that is compact enough to fit in ordinary data center racks.

As part of an EU-funded project called AQTION, a group of scientists from the University of Innsbruck in Austria successfully set up a fully functional ion trap quantum computer into two 19-inch server racks, as typically found in data centers around the world. The device only requires a single wall-mounted power plug and is otherwise self-contained.

The prototype is an exciting development in an industry that relies mostly on lab-based implementations, where quantum computers can only be controlled thanks to purpose-built infrastructure. Developing a set-up that is more accessible is therefore key to expanding the reach of the technology.

This is why the EU recently launchedAQTION, a 10 million project that aims to create a compact ion-trap quantum computer that meets industry standards without needing an ultra-stable lab environment for operation.

"Our quantum computing experiments usually fill 30- to 50-square-meter laboratories," says Thomas Monz, AQTION project coordinator. "We were now looking to fit the technologies developed here in Innsbruck into the smallest possible space while meeting standards commonly used in industry."

The new device, said the research team, shows that quantum computers will soon be ready for use in data centers.

The researchers used ions, which are single-charged atoms, as qubits. Quantum information is encoded in the electronic state of ions, and operations are performed with laser pulses that modify and control the state of the particles.

While the approach differs from the well-known superconducting qubits used by IBM and Google in their quantum computers, ion trap devices are gaining attention in the industry. Honeywell, for instance,made its quantum debut last year with trapped-ion technology.

To fit in a couple of 19-inch racks, every individual building block of AQTION's quantum computer had to be downsized, from the ion trap processor to the vacuum chamber. The biggest challenge, therefore, was to ensure that the device did not compromise on performance but the researchers are confident that their prototype is already delivering promising results.

Even outside of the controlled environment that can be achieved in a lab, the device was stable enough to operate without interruption from external disturbances, and the physicists were able to individually control and entangle up to 24 ions. Measurements showed that the system's performance and error rate were on par with lab-based implementations.

"We were able to show that compactness does not have to come at the expense of functionality," said Christian Marciniak, researcher at the University of Innsbruck.

By next year, the team is expecting to create a device with up to 50 individually controllable qubits.

For now, however, the prototype's hardware and software capabilities will be further upgraded before it is made available online. Researchers will access the device over the cloud to test quantum algorithms on a hardware-agnostic quantum computing language.

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Quantum computers take up a lot of space. Researchers decided to shrink this one down - ZDNet

Elevate your enterprise data technology and strategy at Transform 2021.

The thing that really attracted me to Formula 1 is that its always been about data and technology, says Graeme Hackland, Williams Group IT director and chief information officer of Williams Racing.

Since joining the motorsport racing team in 2014, Hackland has been putting that theory into practice. He is pursuing what he refers to as a data-led digital transformation agenda that helps the organizations designers and engineers create a potential competitive advantage for the teams drivers on race day.

Hackland explains to VentureBeat how Williams F1 is looking to exploit data to make further advances up the grid and how emerging technologies, such as artificial intelligence (AI) and quantum computing, might help in that process.

This interview has been edited for clarity.

VentureBeat: Whats the aim of your data-led transformation process?

Graeme Hackland: Ten years ago, we might have been putting four major package upgrades on the car a year. Were now able to do that much more quickly, and we dont have to wait for big packages of changes. Our digital transformation has been focused on shortening that life cycle. Thats about getting something from a designers brain onto the car as quickly as possible. Test it on a Friday; if its good, it stays. If its not, we refine it, and just keep doing that through the season. And that process has gone really well.

VentureBeat: What kind of data technology are you using to support that process?

Hackland: Some of it is what you would in some industries consider standard data warehousing and business intelligence tools. Some of that is written in-house. At the moment, I dont have a piece of middleware that lies across the whole layer. But thats where we want to head to, so that absolutely everything is feeding into that.

VentureBeat: What would that piece of middleware look like?

Hackland: We originally thought of three main domains: design, manufacturing, and race engineering. And you would have these three bubbles that would all talk to each other. But what weve realized is trying to create data lakes just hasnt worked. It hasnt given us the actual intelligence that we wanted, so we often refer to data puddles. Its much better to have many of these puddles that are well-structured and the data is well understood. And then, through a middleware layer, we can get to the graphical user interfaces.

VentureBeat: What does that layer of information mean for the Williams F1 teams engineers?

Hackland: Were covering everything, from what they look at through to the data structure. And the data structure has been one of our biggest challenges. We relied heavily on Microsoft Excel, and pulling data from all these other sources into Excel was very manual it took too long. So thats the piece of work that weve been doing. Weve not made it public who were working with in that area. Talking publicly about some of the stuff were doing around data and computation, were just not ready yet.

VentureBeat: How do you work out the build vs. buy question?

Hackland: When I got to Williams, we were largely buy-only. We built an in-house capability across three groups: manufacturing, aerodynamics, and race engineering. So they have embedded development groups, and I think thats really important. We considered whether we were going to create a centralized development function. But actually, we feel having them in those three groups is really important. And then as you build those groups, the pendulum swings from buy-only because youve got the capability in-house. The default now is that we will always develop our own if we can. Where theres a genuine competitive advantage, wed develop it ourselves.

VentureBeat: Where might you choose to buy data technologies?

Hackland: Some of the tools that we use trackside are off-the-shelf. Its not all in-house-written, because it doesnt make sense to write your own in some areas. But if you dont write your own applications, youre also accepting that these applications are used by multiple teams. If its a race-engineering application, its probably used across Formula 1 and maybe in other formulas as well. So then you cant customize it and you cant get competitive advantage out of it because everyone else has access to it too. So sometimes well use those as maybe a front end and then well be doing other things in the background. When you start to combine that data with other information, thats when theres a real competitive advantage, and thats where weve put our internal resources.

VentureBeat: What about AI? Is that a technology youre investigating?

Hackland: None of the teams are talking about AI except in passing; theyre just mentioning that AI is being used. None of us want to talk about it yet, and where were applying it. But what weve said publicly is that there are some really interesting challenges that AI can logically be applied to and you get benefits straightaway. So pit stops, the rulebook there are roles that AI can play.

VentureBeat: Can you give me a sense of how AI might be applied in F1?

Hackland: Initially, to augment humans to give engineers more accurate data to work with, or to shortcut their decision-making process so that they can make the right decision more frequently. I felt, even five years ago, that it would be possible that AI could make a pit stop decision without any human intervention. So that is possible, but I dont believe any of the teams will be doing it this year, and we wont. The engineers are not ready, and the humans are not ready to be replaced by AI. So that might take a little bit of time to show them that we can. I think theres still that reluctance to completely hand over the decision-making process, and I can understand that.

VentureBeat: What about other areas of emerging technology?

Hackland: From my perspective, quantum computing is a really exciting opportunity to take computation to a whole new level. And if we can get in there early before the other teams, I think well have a real advantage. There are interesting things happening with some [racing] organizations around that. Once again, were not talking about it publicly, but quantum is completely awesome. I think quantum will take a while. I dont want to be sitting here saying that in the next two years that were going to be developing, designing, and running the car and doing the race analytics on a quantum computer. But a hybrid computer that has quantum elements to it? Absolutely, and within a couple of years. Im really excited about what were doing already.

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Williams F1 drives digital transformation in racing with AI, quantum - VentureBeat

June 2, 2021• Physics 14, s72

Merging ideas from neuroscience, machine learning, and quantum technology, researchers propose a new information-storage device.

Many recent computing advances derive their inspiration from models of the human brain. For example, researchers have created a machine-learning model that mimics the brains ability to recognize new patterns by recalling previously encountered ones. So far, implementations of associative memory have largely involved conventional silicon-chip-based computers. Now, Benjamin Lev of Stanford University and colleagues propose a way of implementing associative memory with multiple Bose-Einstein condensates (BECs) and an optical cavity. The researchers say that their method should be better at learning and recognizing patterns than the standard associative memory design.

A computer with associative memory stores information in a mathematical function that looks like a potential energy landscape with many local minima. Each local minimum corresponds to a separate piece of information. To retrieve that information, the device is initialized in some state close to the relevant minimum, and it then finds that minimum. This process effectively reconstructs data from imprecise versions of that data. While everyday technologies typically dont use associative memory techniques, researchers are interested in them because of their speed and their robustness to user mistakes.

The researchers proposed device stores information in the energy landscape of multiple, separated BECs contained within the same optical cavity. The spin of each BEC interacts with that of the others by scattering photons in the cavity. They can engineer the energy landscape of the system by manipulating the position of each BEC. To retrieve information, the BECs are collectively initialized in a particular spin state, which relaxes into an energy minimum that is imaged using light emitted from the cavity. The researchers think they can build this device in the near term, as they have already demonstrated all the elements in the design.

Sophia Chen

Sophia Chen is a freelance science writer based in Columbus, Ohio.

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A Computer Memory Based on Cold Atoms and Light - Physics

The company said it contracted the expert under a one-year agreement to research tangible and disruptive optimisations that can be made within Bitcoin mining which could result in faster execution and energy savings

Quantum Blockchain PLC () said it signed a service agreement with a UK-based international cryptography expert specialising in cryptocurrency mining blockchain optimisations as part of a research & development (R&D) strategy for Bitcoin mining.

The AIM-listed company saidthere are tangible and disruptive optimisations that can be made within the Bitcoin mining process that could result in faster execution and energy savings. The first set of optimisations is expected to be ready for testing in the coming weeks.

Quantum also said the aim of the work is to improve the efficiency of Bitcoin mining by targeting a material reduction in energy usage and faster hash processing. Thiswill increase the probability of successful mining operations. The firmintends to apply for patents over any relevant intellectual property generated during the process.

Meanwhile, the group said existing and imminent mining optimisations are expected to be made available on commercial cores within the next three months, on field-programmable gate array (FPGA) computer chips within six months and ultimately on application-specific integrated circuit (ASIC) chips in the second half of 2022.

Quantum said this line of R&D represents one of its out-of-the-box solutions to achieve highly competitive Bitcoin mining results, adding it willexplore other proof-of-work cryptos and their competitive mining advantages alongside the cryptography expert.

As part of the service agreement, the firm said it has awarded the consultant options over 10mln new shares in the company exercisable at 5p each between February 15, 2022, and August 15, 2022.

Securing the services of an international expert, who already has significant experience and know-how in Bitcoin mining optimisations, is one of the first concrete moves by the company to challenge the substantial Bitcoin market, Quantums chief executive and chairman Francesco Gardin said in a statement.

The company is addressing, in parallel, other cutting-edge approaches to Bitcoin mining, including, among others, the use of quantum computing. More detailed announcements will be made in due course. We believe that this out-of-the-box approach gives us the potential to play a disruptive role in the Bitcoin mining industry, the CEO added.

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Quantum Blockchain inks deal with cryptography expert to optimise Bitcoin mining operations - Proactive Investors UK

COLLEGE PARK Over the past few decades, quantum computing has developed from what many considered a science-fiction fantasy into what could be the next technological revolution. One local company, College Park-based IonQ Inc., could play a key role.

In what some are calling the quantum space race, governments around the globe are funding quantum computing research in an effort to become the worlds leading innovator. China spends about $2.5 billion on quantum research annually, more than 10 times what the U.S. spends, according to a report in The Wall Street Journal.

The quantum competition, reminiscent of the U.S.-Soviet era Sputnik space race, is expected to heat up under the Biden Administration, which plans to commit $180 billion to research and development and industries of the future, including quantum computing. That spending could provide a boost for IonQ, which was founded just six years ago.

Both Congress and the president have made clear they plan to invest in the research, technology and talent needed to keep the United States in the global vanguard of innovation, said Kara Sibbern, a IonQ spokesperson. At IonQ, we will be working with policymakers to support this effort however we can.

But whether new companies such as IonQ can compete in the brave new world of quantum computing is unclear. IonQ will be up against many U.S. and international companies, including heavy hitters like Google, Microsoft and IBM Corp.

Investors on Wall Street are closely watching the young company. In March, IonQ filed with the Securities and Exchange Commission to go public on the New York Stock Exchange by merging with dMY Technology Group Inc. III, a special-purpose acquisition company, or SPAC, based in Nevada. The deal is valued at about $2 billion. If the SEC approves the transaction, IonQ would be the first company in the U.S. focused specifically on quantum computing to go public.

Like many other companies that use SPACs to raise capital, IonQ is hoping that merging with an acquisition company will allow it to raise capital faster than by using a traditional initial public offering. Merging with dMY affords us greater speed to market, flexibility and ability to focus on business execution, said Sibbern. The deal is expected to be completed this year, but the company couldnt provide an exact date.

The stock offering marks a huge step for a computing technology that not long ago was widely thought to have little promise beyond the theoretical.

Quantum computers use the power of quantum physics to quickly solve problems and perform tasks faster than a conventional computer. The technology could speed up calculations related to finance, drug development, materials discovery, artificial intelligence and others.

Quantum computers function differently from conventional computers, which accounts for their speed. Conventional computers use a large number of tiny transistors, which represent information as either a 1 or a 0. Quantum computers differ in that they use qubits, which can represent and work with both numbers simultaneously. This is due to whats known as superpositioning.

To understand the principle of quantum superpositioning, it is often compared to a coin. Think of a single, stationary coin sitting on a table. It will be in only one of two states: heads or tails. Similarly, a transistor can only be either 0 or 1. But if you spin the coin, you can say its both heads and tails at the same time until the moment you stop it and see what it lands on. This is like a qubit. Until you measure it as a 0 or 1, it can exist in several different states at the same time.

So far, quantum computing is still in the research phase, far from widespread commercial use. IBM unveiled its first commercial quantum computer in 2019, IBM Q System One, but the device is not for sale. Rather, its a cloud-based product that customers can access over the internet to perform calculations.

However, executives at IonQ and other companies believe the industry is close to developing scalable products that can serve business needs.

We believe quantum computing will power the next technological revolution for humankind and that the dawn of the quantum age is here, said Chris Monroe, who co-founded IonQ and serves as the companys chief scientist. Like the information age, quantum is expected to have far-reaching impacts across every facet of our society.

According to Monroe, any corporation with an optimization problem can yield results from quantum computing. Were seeing exciting advances in artificial intelligence by applying quantum to machine learning, which can lead to even greater results, he said.

Important hurdles still exist for quantum. Eddy Zervigon, CEO of Quantum Xchange, a quantum-focused cybersecurity company in Bethesda said that while quantum computers can potentially lead to significant advancements, their speed and power could make it easier for hackers to break into the systems because current encryption methods wont be able to keep up.

This critical point in quantum computing is known as Q-Day, or the day in which quantum computers can render current encryption methods useless. No one is questioning if, but when this day will come, said Zervigon. Quantum Xchange, recently named one of the 20 most promising startups by Technical.ly DC, is dedicated to preparing companies and organizations for Q-Day by offering quantum-safe data protection.

IonQ was founded in 2015 by Monroe and Jungsang Kim. Both are professors in electrical engineering and computer engineering at Duke University in North Carolina. Both have also taught at the University of Maryland and are currently visiting professors in Marylands Physics Department.

The two previously spent more than two decades combined researching quantum physics and engineering. Monroe and Kim would later combine their efforts to publish a scientific paper, Scaling the Ion Trap Quantum Processor, which was published in 2013, and detailed how to build and scale a programmable quantum computer. The paper was noticed by Harry Weller, a venture capitalist with the Maryland-based New Enterprise Association, which provided IonQ with $2 million in seed money.

Between 2015 and 2018, IonQ raised an additional $20 million in funding from Google Ventures, Amazon Web Services and NEA. IonQ would later raise more than $55 million from investors such as Samsung Group, Lockheed Martin Corp. and others.

If IonQ succeeds, it could foster growth of other quantum computing and related companies in Maryland and the Washington area and drive billions of dollars of economic improvement over the next decade in the region, said Monroe.

He added that the University of Marylands support is contributing to the industrys growth. In 2020, IonQ opened a new Quantum Data Center, a 23,000-square-foot center in Marylands Discovery District. The site was made possible in part due to a $5.5 million investment from the university.

Charles Winthrop Clark, a fellow at the National Institute of Standards and Technology and the Joint Quantum Institute, offered a more measured but still optimistic view of quantum computings potential in the region. He notes that while a quantum industry wont do for the Washington metro region what the digital revolution did for Silicon Valley, there will be a lively quantum ecosystem in the DMV.

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Big bang theory: Maryland company moves ahead in quantum space race - The Star Democrat