Predicting many properties of a quantum system from very few measurements
Predicting the properties of complex, large-scale quantum systems is essential for developing quantum technologies. We present an efficient method for constructing an approximate classical description of a quantum state using very few measurements of the state. This description, called a ‘classical...
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Published in | Nature physics Vol. 16; no. 10; pp. 1050 - 1057 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
London
Nature Publishing Group UK
01.10.2020
Nature Publishing Group |
Subjects | |
Online Access | Get full text |
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Abstract | Predicting the properties of complex, large-scale quantum systems is essential for developing quantum technologies. We present an efficient method for constructing an approximate classical description of a quantum state using very few measurements of the state. This description, called a ‘classical shadow’, can be used to predict many different properties; order
log
(
M
)
measurements suffice to accurately predict
M
different functions of the state with high success probability. The number of measurements is independent of the system size and saturates information-theoretic lower bounds. Moreover, target properties to predict can be selected after the measurements are completed. We support our theoretical findings with extensive numerical experiments. We apply classical shadows to predict quantum fidelities, entanglement entropies, two-point correlation functions, expectation values of local observables and the energy variance of many-body local Hamiltonians. The numerical results highlight the advantages of classical shadows relative to previously known methods.
An efficient method has been proposed through which the properties of a complex, large-scale quantum system can be predicted without fully characterizing the quantum state. |
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AbstractList | Predicting the properties of complex, large-scale quantum systems is essential for developing quantum technologies. We present an efficient method for constructing an approximate classical description of a quantum state using very few measurements of the state. This description, called a ‘classical shadow’, can be used to predict many different properties; order
log
(
M
)
measurements suffice to accurately predict
M
different functions of the state with high success probability. The number of measurements is independent of the system size and saturates information-theoretic lower bounds. Moreover, target properties to predict can be selected after the measurements are completed. We support our theoretical findings with extensive numerical experiments. We apply classical shadows to predict quantum fidelities, entanglement entropies, two-point correlation functions, expectation values of local observables and the energy variance of many-body local Hamiltonians. The numerical results highlight the advantages of classical shadows relative to previously known methods.
An efficient method has been proposed through which the properties of a complex, large-scale quantum system can be predicted without fully characterizing the quantum state. Predicting the properties of complex, large-scale quantum systems is essential for developing quantum technologies. We present an efficient method for constructing an approximate classical description of a quantum state using very few measurements of the state. This description, called a ‘classical shadow’, can be used to predict many different properties; order log(M) measurements suffice to accurately predict M different functions of the state with high success probability. The number of measurements is independent of the system size and saturates information-theoretic lower bounds. Moreover, target properties to predict can be selected after the measurements are completed. We support our theoretical findings with extensive numerical experiments. We apply classical shadows to predict quantum fidelities, entanglement entropies, two-point correlation functions, expectation values of local observables and the energy variance of many-body local Hamiltonians. The numerical results highlight the advantages of classical shadows relative to previously known methods.An efficient method has been proposed through which the properties of a complex, large-scale quantum system can be predicted without fully characterizing the quantum state. |
Author | Kueng, Richard Preskill, John Huang, Hsin-Yuan |
Author_xml | – sequence: 1 givenname: Hsin-Yuan orcidid: 0000-0001-5317-2613 surname: Huang fullname: Huang, Hsin-Yuan email: hsinyuan@caltech.edu organization: Institute for Quantum Information and Matter, California Institute of Technology, Department of Computing and Mathematical Sciences, California Institute of Technology – sequence: 2 givenname: Richard surname: Kueng fullname: Kueng, Richard organization: Institute for Quantum Information and Matter, California Institute of Technology, Department of Computing and Mathematical Sciences, California Institute of Technology, Institute for Integrated Circuits, Johannes Kepler University Linz – sequence: 3 givenname: John surname: Preskill fullname: Preskill, John organization: Institute for Quantum Information and Matter, California Institute of Technology, Department of Computing and Mathematical Sciences, California Institute of Technology, Walter Burke Institute for Theoretical Physics, California Institute of Technology |
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Snippet | Predicting the properties of complex, large-scale quantum systems is essential for developing quantum technologies. We present an efficient method for... |
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SubjectTerms | 639/705 639/766/259 639/766/483 639/766/483/481 639/766/483/640 Approximation Atomic Circuits Classical and Continuum Physics Complex Systems Condensed Matter Physics Entropy Hamiltonian functions Information theory Lower bounds Mathematical and Computational Physics Molecular Optical and Plasma Physics Physics Physics and Astronomy Properties (attributes) Quantum entanglement Quantum theory Shadows Theoretical Tomography |
Title | Predicting many properties of a quantum system from very few measurements |
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