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Quantum Computing: Designing Next-Generation Automotive Batteries

By Dick Weisinger

Researchers predict that in a decade quantum computers will begin to become available for solving previously intractable problems. That’s not stopping another group of researchers from experimenting and using primitive first-generation quantum computers that are available already today.

The hope is to be able to speed up calculations for difficult problems while also beginning to learn how quantum computers of the future will need to programmed. IBM and Daimler have teamed up on a project that applies existing quantum computers to the problem of improving automotive batteries.

Heike Riel, IBM Research quantum lead, said that car batteries will be one of the first projects using quantum simulation. “Soon, the number of qubits will be enough to generate valuable insights with which you can develop new materials. We’ll see quantum advantage soon in the area of quantum simulation and new materials.”

Tyler Takeshita, Daimler’s former quantum technology expert and now researcher at Amazon AWS, said that “we are at the very infancy of the technology. Today, you don’t have long before you lose control of the quantum system and its interaction with the environment takes over. And now the results from the quantum computer are no longer useful. The goal everyone is chasing is to have enough control over these quantum systems to perform reliable calculations on meaningful applications.”

Alan Ho, Jarrod McClean, and Shyue Ping Ong, quantum computer researchers, wrote in Joule that “moreover, these computers open possibilities that have few to no current analogs in classical simulation. They are able to efficiently simulate the complex dynamics of strongly correlated, excited state, and complex interfacial systems that are necessary to achieve an accurate simulation of modern devices such as solar cells, batteries, and energy networks. Dynamics in these regimes offer a microscopic view of these systems that experiments cannot presently dream of, at an accuracy not currently possible with today’s simulation techniques. These insights have the potential to change the design landscape for many of these materials.

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