A new Caltech-led study 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 trickiest physics and chemistry problems.
“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, the Richard P.
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.
“Our brains and our computers are classical, and this limits our ability to interact with and understand the quantum reality.”.
While previous studies have shown that machine learning models have the ability to solve some quantum problems, these methods typically operate in ways that make it difficult for researchers to learn how the machines arrived at their solutions.
“Normally, when it comes to machine learning, you don’t know how the machine solved the problem.
“The worry was that people creating new quantum states in the lab might not be able to understand them,” Preskill explains.
“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 very much about that state in advance.”.
Ultimately, of course, future quantum-based machine learning tools will outperform classical methods, the scientists say.
In a related study appearing June 10, 2022, in Science, Huang, Preskill, and their collaborators report using Google’s Sycamore processor, a rudimentary quantum computer, to demonstrate that quantum machine learning is superior to classical approaches.
“But we do know that quantum machine learning will eventually be the most efficient.”.