Using Machine Learning to Unlock the Power of Quantum Physics on Traditional Computers

Quantum problems can be solved by traditional computers

Quantum computers have been the subject of much discussion, and with good reason. These futuristic computers mimic nature’s microscopic processes, which can help us better understand quantum physics and discover new materials. Experts say that viable quantum computers will not be available for at least a decade. What can researchers do in the interim?

In a new study led by Caltech, published in Science, researchers describe how machine-learning tools run on classic computers can be used to predict quantum systems, and help solve some of physics’ and chemistry’s most difficult problems. This notion has been demonstrated experimentally, but the new report is first to mathematically demonstrate that the method actually works.

\”Quantum computer are ideal for many types physics and materials problems,\” said Hsin Yuan (Robert), the lead author, a student who works with John Preskill. Preskill is the Richard P. Feynman professor of theoretical physics and the Allen V. C. Davis and Lenabelle Davis leadership chair of the Institute for Quantum Science and Technology. We aren’t there yet, and were surprised to find that machine learning techniques can be used until then. This paper is ultimately about what humans can discover about the physical universe.