Quantum advantage in learning from experiments
- Institute for Quantum Information and Matter, Caltech, Pasadena, CA, USA.; Department of Computing and Mathematical Sciences, Caltech, Pasadena, CA, USA.
- Google Quantum AI, Venice, CA 90291, USA.
- Harvard Society of Fellows, Cambridge, MA 02138, USA.; Black Hole Initiative, Cambridge, MA 02138, USA.
- Department of Electrical Engineering and Computer Science, University of California Berkeley, Berkeley, CA, USA.; Simons Institute for the Theory of Computing, Berkeley, CA, USA.
- Microsoft Research AI, Redmond, WA 98052, USA.
- Institute for Integrated Circuits, Johannes Kepler University Linz, Austria.
- Institute for Quantum Information and Matter, Caltech, Pasadena, CA, USA.; Department of Computing and Mathematical Sciences, Caltech, Pasadena, CA, USA.; AWS Center for Quantum Computing, Pasadena, CA 91125, USA.
Quantum technology promises to revolutionize how we learn about the physical world. An experiment that processes quantum data with a quantum computer could have substantial advantages over conventional experiments in which quantum states are measured and outcomes are processed with a classical computer. We proved that quantum machines could learn from exponentially fewer experiments than the number required by conventional experiments. This exponential advantage is shown for predicting properties of physical systems, performing quantum principal component analysis, and learning about physical dynamics. Furthermore, the quantum resources needed for achieving an exponential advantage are quite modest in some cases. Conducting experiments with 40 superconducting qubits and 1300 quantum gates, we demonstrated that a substantial quantum advantage is possible with today’s quantum processors.
- Research Organization:
- Harvard Univ., Cambridge, MA (United States); California Institute of Technology (CalTech), Pasadena, CA (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC)
- DOE Contract Number:
- SC0007870; SC0020290; NA0003525
- OSTI ID:
- 1980744
- Journal Information:
- Science, Vol. 376, Issue 6598; ISSN 0036-8075
- Publisher:
- AAAS
- Country of Publication:
- United States
- Language:
- English
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