There has been a lot of buzz about quantum computers and for good reason. Futuristic computers are designed to mimic what happens in nature at the microscopic scale, meaning they have the power to better understand the quantum realm and accelerate the discovery of new materials, including pharmaceuticals, environmentally friendly chemicals the environment and much 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, which run on classical computers, can be used to make predictions about quantum systems, helping researchers solve some of the trickiest problems in physics and chemistry Although this notion has been proposed before, the new report is the first to mathematically demonstrate that the method works on problems that no traditional algorithm could solve.
“Quantum computers are ideal for many types of problems in physics and materials science,” says lead author Hsin-Yuan (Robert) Huang, a graduate student working with the John Preskill Professor of Theoretical Physics Richard P .Feynman and 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 we were surprised to learn that classical machine learning methods can be used in the meantime. Ultimately, this paper is about showing 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 at the same time. And an overlap of states can lead to entanglement, a phenomenon in which particles are linked, or correlated, without even being in contact with each other. These strange states and connections, which are widespread in 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’s a huge number of atoms, and they’re overlapping and intertwined. You can’t write an equation to describe it all.”
The new study is the first mathematical demonstration 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,” 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 models have the ability to solve some quantum problems, these methods typically work in ways that make it difficult for researchers to learn how the machines arrived at their solutions.
“Usually when it comes to machine learning, you don’t know how the machine solved the problem. It’s a black box,” Huang says. “But now we’ve basically figured out what’s going on in the box through our mathematical analysis and numerical simulations.” Huang and his colleagues performed extensive numerical simulations in collaboration with Caltech’s AWS Center for Quantum Computing, which corroborated 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 creating new quantum states in the lab might not be able to understand them,” Preskill explains. “But now we can get reasonable classical data to explain what’s going on. Classical machines don’t just give us an answer like an oracle, they guide us to a deeper understanding.”
Co-author Victor V. Albert, a physicist at NIST (National Institute of Standards and Technology) and a former DuBridge Award postdoctoral fellow at Caltech, agrees. “The part that excites me 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 requiring you to know a lot about that state beforehand.”
Ultimately, of course, future quantum-based machine learning tools will outperform classical methods, scientists say. In a