Quantum advantage in learning from experiments
Abstract
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.
Additional Information
© 2022 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. Submitted 17 December 2021; accepted 14 April 2022. The quantum hardware used for this experiment was developed by the Google Quantum AI hardware team, under the direction of A. Megrant, J. Kelly, and Y. Chen. Methods for device calibrations were developed by the physics team led by V. Smelyanskiy. Data were collected via cloud access through Google's Quantum Computing Service. We thank B. Foxen for special support and maintaining the device to the caliber needed to complete the experiments. Funding: H.H. is supported by a Google PhD Fellowship. J.C. is supported by a Junior Fellowship from the Harvard Society of Fellows, by the Black Hole Initiative, and in part by the Department of Energy under grant DE-SC0007870. S.C. is supported by the National Science Foundation under Award 2103300 and was visiting the Simons Institute for the Theory of Computing while part of this work was completed. J.P. acknowledges funding from the US Department of Energy Office of Science Office of Advanced Scientific Computing Research (DE-NA0003525, DE-SC0020290), and the National Science Foundation (PHY-1733907). The Institute for Quantum Information and Matter is an NSF Physics Frontiers Center. Author contributions: H.H., J.C., S.C., J.L., R.K., J.P., and J.M. were involved in conceptualization, planning, and theoretical developments. H.H., M.B., M.M., H.N., R.B., and J.M. contributed to the design and execution of the experiments on the Google processor. All authors were involved in the writing and presentation of the work. Competing interests: The authors declare that they have no competing interests. Data and materials availability: In addition to the data in the paper and supplemental materials, code related to this experiment is hosted at Github (35). The data needed to reproduce figures are hosted at Zenodo (36). All other data needed to evaluate the conclusions in the paper are present in the paper or the supplementary materials.Attached Files
Submitted - 2112.00778.pdf
Supplemental Material - science.abn7293_sm.pdf
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Additional details
- Eprint ID
- 112896
- Resolver ID
- CaltechAUTHORS:20220113-234532429
- Harvard Society of Fellows
- Black Hole Initiative
- DE-SC0007870
- Department of Energy (DOE)
- DMS-2103300
- NSF
- DE-NA0003525
- Department of Energy (DOE)
- DE-SC0020290
- Department of Energy (DOE)
- PHY-1733907
- NSF
- Created
-
2022-01-14Created from EPrint's datestamp field
- Updated
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2023-02-28Created from EPrint's last_modified field
- Caltech groups
- Institute for Quantum Information and Matter, AWS Center for Quantum Computing