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Quantum advantage in learning from experiments

Huang, Hsin-Yuan and Broughton, Michael and Cotler, Jordan and Chen, Sitan and Li, Jerry and Mohseni, Masoud and Neven, Hartmut and Babbush, Ryan and Kueng, Richard and Preskill, John and McClean, Jarrod R. (2021) Quantum advantage in learning from experiments. . (Unpublished)

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Quantum technology has the potential to revolutionize how we acquire and process experimental data to learn about the physical world. An experimental setup that transduces data from a physical system to a stable quantum memory, and processes that data using a quantum computer, could have significant advantages over conventional experiments in which the physical system is measured and the outcomes are processed using a classical computer. We prove that, in various tasks, quantum machines can learn from exponentially fewer experiments than those required in conventional experiments. The exponential advantage holds in predicting properties of physical systems, performing quantum principal component analysis on noisy states, and learning approximate models of physical dynamics. In some tasks, the quantum processing needed to achieve the exponential advantage can be modest; for example, one can simultaneously learn about many noncommuting observables by processing only two copies of the system. Conducting experiments with up to 40 superconducting qubits and 1300 quantum gates, we demonstrate that a substantial quantum advantage can be realized using today's relatively noisy quantum processors. Our results highlight how quantum technology can enable powerful new strategies to learn about nature.

Item Type:Report or Paper (Discussion Paper)
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URLURL TypeDescription Paper ItemOpen-Source code
Huang, Hsin-Yuan0000-0001-5317-2613
Cotler, Jordan0000-0003-3161-9677
Neven, Hartmut0000-0002-9681-6746
Babbush, Ryan0000-0001-6979-9533
Kueng, Richard0000-0002-8291-648X
Preskill, John0000-0002-2421-4762
McClean, Jarrod R.0000-0002-2809-0509
Additional Information:Attribution 4.0 International (CC BY 4.0). The quantum hardware used for this experiment was developed by the Google Quantum AI hardware team, under the direction of Anthony Megrant, Julian Kelly and Yu Chen. Methods for device calibrations were developed by the physics team led by Vadim Smelyanskiy. Data was collected via cloud access through Google’s Quantum Computing Service. HH is supported by a Google PhD Fellowship. JC is supported by a Junior Fellowship from the Harvard Society of Fellows, the Black Hole Initiative, as well as in part by the Department of Energy under grant DE-SC0007870. SC 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. JP acknowledges funding from the U.S. 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.
Group:Institute for Quantum Information and Matter, AWS Center for Quantum Computing
Funding AgencyGrant Number
Harvard Society of FellowsUNSPECIFIED
Black Hole InitiativeUNSPECIFIED
Department of Energy (DOE)DE-SC0007870
Department of Energy (DOE)DE-NA0003525
Department of Energy (DOE)DE-SC0020290
Record Number:CaltechAUTHORS:20220113-234532429
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:112896
Deposited By: George Porter
Deposited On:14 Jan 2022 17:05
Last Modified:14 Jan 2022 17:05

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