Power

D-Wave’s most powerful quantum computer is limited, but ready

Companies that need help solving some complicated optimization problems will be first in line to use the new 5,000-qubit D-Wave Advantage service. A more capable quantum computer is still a long way away.

D-Wave’s most powerful quantum computer is limited, but ready

The D-Wave Advantage quantum computer.

Photo: D-Wave

D-Wave is ready for early adopters of quantum computing to start using its Advantage quantum computer, which is now generally available and promises twice as much capability as its earlier quantum systems.

First announced last year, the Advantage computer claims 5,000 qubits and more than twice as much connectivity between qubits as D-Wave's 2000Q quantum computer. It will be offered through D-Wave's Leap cloud service, although the company will build systems for customers who want to run them in their own data centers if asked, said Alan Baratz, CEO of D-Wave.

"What we want to be able to do is allow our customers to solve their problems better than they are solving them today," Baratz said. "Better could be faster, it could be higher-quality solutions, it could be more cost-effectively, but we want to deliver customer advantage and customer value."

D-Wave's approach to quantum computing is somewhat controversial in this emerging field. The company's computers rely on an approach called quantum annealing, which manipulates qubits into quantum positions that can represent many more than two values, the limitation of classical computers. This approach then allows the qubits to lose their quantum state naturally and, depending on the problem it is being asked to solve, settle into their lowest-energy state. If the problem is correctly formulated, that low-energy state represents the optimal solution to the problem.

This is different from the quantum-gate approach that companies like IBM, Google, Honeywell and others are taking and which is not expected to generate production-quality systems for several years. Quantum gate computers hold qubits in a quantum state for an extended period of time while performing calculations, but it is extremely difficult to maintain that state without enormous machines and temperatures near absolute zero, challenges that have limited the production of quantum gate machines.

"A 1,000-qubit gate-based machine is going to do a lot more than a 5,000-qubit quantum annealer," said Paul Smith-Goodson, senior quantum analyst for Moor Insights and Strategy. Those 1,000-qubit machines are a long way off being ready, however, and in the meantime it will take some experimentation to determine the possibilities for D-Wave's latest machine, he said.

D-Wave's Baratz allowed that the company's customers are mostly building "preproduction" applications for the new service that are targeted at narrow parts of their business. That's partly due to performance limitations but also because quantum computing of any sort presents a steep software-development learning curve. He believes that D-Wave's approach allows customers to reap some of the performance benefits of quantum computing without having to wait for systems based around the other approach.

"There isn't a single quantum computer today that allows you to do something that you can't do classically," Baratz said. However, D-Wave's Advantage system helped Save-On Foods reduce the time it took to run an "important optimization task" from 25 hours to two minutes, the company said in a release, and Baratz believes the promise of those kinds of performance improvement will draw paying customers.

Customers can use the Advantage computer in two ways through the Leap service. They can pay $2,000 an hour to access the quantum processor or $100 an hour to access a hybrid quantum-classical computer, which combines the Advantage system alongside standard processors and graphics chips from traditional vendors.

Fintech

Judge Zia Faruqui is trying to teach you crypto, one ‘SNL’ reference at a time

His decisions on major cryptocurrency cases have quoted "The Big Lebowski," "SNL," and "Dr. Strangelove." That’s because he wants you — yes, you — to read them.

The ways Zia Faruqui (right) has weighed on cases that have come before him can give lawyers clues as to what legal frameworks will pass muster.

Photo: Carolyn Van Houten/The Washington Post via Getty Images

“Cryptocurrency and related software analytics tools are ‘The wave of the future, Dude. One hundred percent electronic.’”

That’s not a quote from "The Big Lebowski" — at least, not directly. It’s a quote from a Washington, D.C., district court memorandum opinion on the role cryptocurrency analytics tools can play in government investigations. The author is Magistrate Judge Zia Faruqui.

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Veronica Irwin

Veronica Irwin (@vronirwin) is a San Francisco-based reporter at Protocol covering fintech. Previously she was at the San Francisco Examiner, covering tech from a hyper-local angle. Before that, her byline was featured in SF Weekly, The Nation, Techworker, Ms. Magazine and The Frisc.

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FTA
The Financial Technology Association (FTA) represents industry leaders shaping the future of finance. We champion the power of technology-centered financial services and advocate for the modernization of financial regulation to support inclusion and responsible innovation.
Enterprise

AWS CEO: The cloud isn’t just about technology

As AWS preps for its annual re:Invent conference, Adam Selipsky talks product strategy, support for hybrid environments, and the value of the cloud in uncertain economic times.

Photo: Noah Berger/Getty Images for Amazon Web Services

AWS is gearing up for re:Invent, its annual cloud computing conference where announcements this year are expected to focus on its end-to-end data strategy and delivering new industry-specific services.

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Donna Goodison

Donna Goodison (@dgoodison) is Protocol's senior reporter focusing on enterprise infrastructure technology, from the 'Big 3' cloud computing providers to data centers. She previously covered the public cloud at CRN after 15 years as a business reporter for the Boston Herald. Based in Massachusetts, she also has worked as a Boston Globe freelancer, business reporter at the Boston Business Journal and real estate reporter at Banker & Tradesman after toiling at weekly newspapers.

Image: Protocol

We launched Protocol in February 2020 to cover the evolving power center of tech. It is with deep sadness that just under three years later, we are winding down the publication.

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Bennett Richardson ( @bennettrich) is the president of Protocol. Prior to joining Protocol in 2019, Bennett was executive director of global strategic partnerships at POLITICO, where he led strategic growth efforts including POLITICO's European expansion in Brussels and POLITICO's creative agency POLITICO Focus during his six years with the company. Prior to POLITICO, Bennett was co-founder and CMO of Hinge, the mobile dating company recently acquired by Match Group. Bennett began his career in digital and social brand marketing working with major brands across tech, energy, and health care at leading marketing and communications agencies including Edelman and GMMB. Bennett is originally from Portland, Maine, and received his bachelor's degree from Colgate University.

Enterprise

Why large enterprises struggle to find suitable platforms for MLops

As companies expand their use of AI beyond running just a few machine learning models, and as larger enterprises go from deploying hundreds of models to thousands and even millions of models, ML practitioners say that they have yet to find what they need from prepackaged MLops systems.

As companies expand their use of AI beyond running just a few machine learning models, ML practitioners say that they have yet to find what they need from prepackaged MLops systems.

Photo: artpartner-images via Getty Images

On any given day, Lily AI runs hundreds of machine learning models using computer vision and natural language processing that are customized for its retail and ecommerce clients to make website product recommendations, forecast demand, and plan merchandising. But this spring when the company was in the market for a machine learning operations platform to manage its expanding model roster, it wasn’t easy to find a suitable off-the-shelf system that could handle such a large number of models in deployment while also meeting other criteria.

Some MLops platforms are not well-suited for maintaining even more than 10 machine learning models when it comes to keeping track of data, navigating their user interfaces, or reporting capabilities, Matthew Nokleby, machine learning manager for Lily AI’s product intelligence team, told Protocol earlier this year. “The duct tape starts to show,” he said.

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Kate Kaye

Kate Kaye is an award-winning multimedia reporter digging deep and telling print, digital and audio stories. She covers AI and data for Protocol. Her reporting on AI and tech ethics issues has been published in OneZero, Fast Company, MIT Technology Review, CityLab, Ad Age and Digiday and heard on NPR. Kate is the creator of RedTailMedia.org and is the author of "Campaign '08: A Turning Point for Digital Media," a book about how the 2008 presidential campaigns used digital media and data.

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