Traditional computers can solve some quantum problems

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There has been a lot of excitement about quantum computers and for good reason. Futuristic computers are designed to mimic what happens in nature on a microscopic scale, which means they have the power to better understand the quantum realm and accelerate the discovery of new materials, including pharmaceuticals, environmentally 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 study by Caltech in the journal science describes how machine learning tools, run on classical computers, can be used to make predictions about quantum systems and thus help researchers solve some of the more complicated problems in physics and chemistry. Although this notion has been shown experimentally before, the new report is the first to mathematically prove that the method works.

“Quantum computers are ideal for many types of physics and materials science problems,” says lead author Hsin-Yuan (Robert) Huang, a graduate student working with John Preskill, Richard P. Feynman professor of theoretical physics. and the Allen VC Davis and Lenabelle Davis Leadership Chair of 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 shows what humans can learn about the physical world.”

At microscopic levels, 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 simultaneously. And a superposition of states can lead to entanglement, a phenomenon in which particles are connected, or correlated, without even being in contact with each other. These strange states and connections, which are widespread within natural and man-made materials, are very difficult to describe mathematically.

“Predicting the low-energy state of a material is very difficult,” says Huang. “There are a huge number of atoms, and they are overlapping and tangled. You can’t write an equation to describe them all.”

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

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

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

“Normally, when it comes to machine learning, you don’t know how the machine solved the problem. It’s a black box,” says Huang. “But now we’ve essentially figured out what’s going on in the box through our numerical simulations.” Huang and his colleagues performed 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 the complex and exotic phases of quantum matter.

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

Co-author Victor V. Albert, a NIST (National Institute of Standards and Technology) physicist and former DuBridge Award postdoctoral fellow at Caltech, agrees. “The part that excites me the most about this work is that we are now closer to a tool that helps you understand the underlying phase of a quantum state without you needing to know much about that state in advance.”

Ultimately, of course, future quantum-based machine learning tools will surpass classical methods, scientists say. In a related study that appeared on June 10, 2022, in scienceHuang, Preskill and their collaborators report that they used Google’s Sycamore processor, a rudimentary quantum computer, to prove that quantum machine learning is superior to classical approaches.

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

the science the study is titled “Efficient demonstrable machine learning for quantum many-body problems”.

The 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.

Provided by the California Institute of Technology

Citation: Traditional Computers Can Solve Some Quantum Problems (2022, September 22) Retrieved September 22, 2022 from

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