Physicists prove that classical machine learning models can improve predictions about quantum materials. Quantum computers have generated a lot of buzz and for good reason. The futuristic computers are designed to mimic what happens in nature at microscopic scales. This means they have the
, 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 within natural and human-made materials, are very difficult to describe mathematically.
“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 the quantum reality.” Google’s Sycamore chip, a quantum computer, is kept cool inside their quantum cryostat. Credit: Eric Lucero/Google, Inc.
Co-author Victor V. Albert, a NIST physicist and former DuBridge Prize Postdoctoral Scholar 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 very much about that state in advance.”
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