Traditional Computers Can Solve Some Quantum Problems

There has been a lot of excitement about quantum computing, and for good reason. Future computers are designed to mimic what happens in nature on a microscopic scale, meaning they have the potential to better understand the quantum realm and accelerate the discovery of new materials, including pharmaceuticals, green chemicals and more. However, experts say, sustainable quantum computers are still a decade or more away. What should researchers do in the meantime?

A new Caltech study in the journal Science describes how machine learning tools running on classical computers can be used to make predictions about quantum systems, helping researchers solve some of the most complicated problems in physics and chemistry. Although this notion has already been proven experimentally, the new report is the first to show mathematically 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 Allen VC Davis. and Allen VC with Davis Lenabelle Davis Executive President of the Institute for Quantum Science and Technology (IQIM). “But we haven’t gotten that far yet, and we were surprised to learn that classical machine learning methods can now be used. Ultimately, this paper shows what humans can learn about the physical world.”

At the microscopic level, the physical world is becoming an incredibly complex place governed by the laws of quantum physics. In this region, particles can be in superimposed states or in two states at the same time. And overlapping states can lead to entanglement, a phenomenon where particles bond or bond without actually being in contact with each other. These strange situations and relationships, which are widespread in natural and artificial materials, are very difficult to describe mathematically.

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

The new research 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 the quantum world,” says Preskill. “Our brains and computers are classical, which limits our ability to interact with and understand quantum reality.”

While previous research has shown that machine learning is capable of solving some quantum problems, these methods generally work in ways that make it difficult for researchers to know how the machines arrived at the 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 in the box through our numerical simulations.” Huang and his colleagues, in collaboration with Caltech’s AWS Center for Quantum Computing, performed extensive numerical simulations that confirmed the theoretical results.

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

“The worry was that people creating new quantum states in the lab wouldn’t be able to understand them,” Preskill explained. “But now we can take classical clues to understand what’s going on. Classical machines don’t just provide answers like an oracle, they lead us to a deeper understanding.”

Co-author Victor V. Albert, a physicist at NIST (National Institute of Standards and Technology) and a former DuBridge Prize postdoctoral fellow at Caltech, agrees. “What excites me most about this work is that we are now closer to a tool that will help you understand the fundamental phase of a quantum state without having to know much about that state.”

Ultimately, scientists say, future quantum-based learning tools will outperform classical methods. In a study published June 10, 2022 in the journal Science , Huang, Preskill and their colleagues report using Google’s Sycamore processor, a basic quantum computer, to show that quantum machine learning is superior to classical approaches.

“We’re still early in this field,” says Huang. “But we know that quantum machine learning will ultimately be the most effective.”

The scientific paper is titled “Probably efficient machine learning for quantum many-body problems”.

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