HomeSciencePhysicsTraditional computers can solve some quantum problems

Traditional computers can solve some quantum problems

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There has been a lot of buzz about quantum computers and for good reason. The futuristic computers are designed to mimic at a microscopic scale what happens in nature, meaning they have the power to better understand the quantum realm and accelerate the discovery of new materials, including pharmaceuticals, eco-friendly chemicals and more. . However, experts say viable quantum computers are still a decade or more away. What should researchers do in the meantime?

A new Caltech-led study in the journal Science describes how machine learning tools, keep going classic computerscan be used to make predictions about quantum systems helping researchers solve some of the most difficult physics and chemistry problems. While this idea has been demonstrated experimentally before, the new report is the first to mathematically prove that the method works.

“Quantum computers are ideal for many kinds of physics and materials science problems,” said lead author Hsin-Yuan (Robert) Huang, a graduate student who works with John Preskill, the Richard P. Feynman Professor of Theoretical Physics and the Allen VC Davis and Lelabelle Davis Leadership Chair. from the Institute for Quantum Science and Technology (IQIM). “But we’re not quite there yet and were surprised to learn that classical machine learning methods can be used in the meantime. Ultimately, this article is about showing what people can learn about the physical world.”

At the microscopic level, the physical world becomes an incredibly complex place governed by the laws of quantum physics. In this realm, particles can exist in a superposition of states, or in two states at once. And a superposition of states can lead to: entanglement, a phenomenon in which particles are connected or correlated with each other, without even contacting each other. These strange states and connections, which are widespread in natural and man-made materials, are mathematically difficult to describe.

“Predicting the low energy state of a material is very difficult,” Huang says. “There are huge numbers of atoms, and they’re superimposed and entangled. You can’t write an equation to describe everything.”

The new study is the first mathematical demonstration that classic machine learning can be used to bridge the gap between us and the quantum world. Machine learning is a type of computer application that human brain learning from data.

“We’re classic creatures living in a quantum world,” says Preskill. “Our brains and our computers are classical, and this limits our ability to interact with and understand quantum reality.”

While previous studies have shown that machine learning applications have the ability to solve a number of quantum problems, these methods typically work in ways that make it difficult for researchers to learn how the machines arrived at their solutions.

“Usually with machine learning, you don’t know how the machine solved the problem. It’s a black box,” Huang says. “But now we’ve essentially figured out what’s going on inside the box through our numerical simulations.” Huang and his colleagues did extensive numerical simulations in collaboration with Caltech’s AWS Center for Quantum Computing, which confirmed their theoretical results.

The new study will help scientists better understand and classify complex and exotic phases of quantum matter.

“The concern was that people who create new quantum states in the lab might not be able to understand them,” explains Preskill. “But now we can obtain reasonable classical data to explain what is going on. The classical machines not only give us an answer like an oracle, but lead us to a deeper understanding.”

Co-author Victor V. Albert, a National Institute of Standards and Technology (NIST) physicist and former DuBridge Prize Postdoctoral Scholar at Caltech, agrees. “The part that excites me most about this work is that we’re now closer to a tool that helps you understand the underlying phase of a quantum state without having to know a whole lot about that state beforehand.”

Ultimately, of course, future quantum-based machine learning tools will outperform classical methods, the scientists say. In a related study to be published on June 10, 2022, in ScienceHuang, Preskill and their collaborators report using Google’s Sycamore processor, a rudimentary computerto demonstrate that quantum machine learning is superior to classical approaches.

“We are still at the beginning of this field,” Huang says. “But we do know that learning quantum machines will ultimately be the most efficient.”

The Science study is titled “Demonstrably efficient machine learning for quantum many-body problems.”


Theory suggests that quantum computers should be exponentially faster at some learning tasks than classical machines


More information:
Hsin-Yuan Huang, demonstrably efficient machine learning for quantum many-body problems, Science (2022). DOI: 10.1126/science.abk3333. www.science.org/doi/10.1126/science.abk3333

Quote: Traditional computers can solve some quantum problems (2022, September 22) retrieved September 22, 2022 from https://phys.org/news/2022-09-traditional-quantum-problems.html

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