Predicting many properties of a quantum system from very few measurements

被引:792
作者
Huang, Hsin-Yuan [1 ,2 ]
Kueng, Richard [1 ,2 ,3 ]
Preskill, John [1 ,2 ,4 ]
机构
[1] CALTECH, Inst Quantum Informat & Matter, Pasadena, CA 91125 USA
[2] CALTECH, Dept Comp & Math Sci, Pasadena, CA 91125 USA
[3] Johannes Kepler Univ Linz, Inst Integrated Circuits, Linz, Austria
[4] CALTECH, Walter Burke Inst Theoret Phys, Pasadena, CA 91125 USA
关键词
STATES;
D O I
10.1038/s41567-020-0932-7
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
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.
引用
收藏
页码:1050 / +
页数:9
相关论文
共 30 条
[1]   Gentle Measurement of Quantum States and Differential Privacy [J].
Aaronson, Scott ;
Rothblum, Guy N. .
PROCEEDINGS OF THE 51ST ANNUAL ACM SIGACT SYMPOSIUM ON THEORY OF COMPUTING (STOC '19), 2019, :322-333
[2]   Shadow Tomography of Quantum States [J].
Aaronson, Scott .
STOC'18: PROCEEDINGS OF THE 50TH ANNUAL ACM SIGACT SYMPOSIUM ON THEORY OF COMPUTING, 2018, :325-338
[3]  
Bonet-Monroig X., 2019, PREPRINT
[4]   Probing Renyi entanglement entropy via randomized measurements [J].
Brydges, Tiff ;
Elben, Andreas ;
Jurcevic, Petar ;
Vermersch, Benoit ;
Maier, Christine ;
Lanyon, Ben P. ;
Zoller, Peter ;
Blatt, Rainer ;
Roos, Christian F. .
SCIENCE, 2019, 364 (6437) :260-+
[5]   Solving the quantum many-body problem with artificial neural networks [J].
Carleo, Giuseppe ;
Troyer, Matthias .
SCIENCE, 2017, 355 (6325) :602-605
[6]   Reconstructing quantum states with generative models [J].
Carrasquilla, Juan ;
Torlai, Giacomo ;
Melko, Roger G. ;
Aolita, Leandro .
NATURE MACHINE INTELLIGENCE, 2019, 1 (03) :155-161
[7]  
Carrasquilla J, 2017, NAT PHYS, V13, P431, DOI [10.1038/nphys4035, 10.1038/NPHYS4035]
[8]   Efficient quantum state tomography [J].
Cramer, Marcus ;
Plenio, Martin B. ;
Flammia, Steven T. ;
Somma, Rolando ;
Gross, David ;
Bartlett, Stephen D. ;
Landon-Cardinal, Olivier ;
Poulin, David ;
Liu, Yi-Kai .
NATURE COMMUNICATIONS, 2010, 1
[9]   LOW-TEMPERATURE PROPERTIES OF THE RANDOM HEISENBERG ANTI-FERROMAGNETIC CHAIN [J].
DASGUPTA, C ;
MA, S .
PHYSICAL REVIEW B, 1980, 22 (03) :1305-1319
[10]   Topological quantum memory [J].
Dennis, E ;
Kitaev, A ;
Landahl, A ;
Preskill, J .
JOURNAL OF MATHEMATICAL PHYSICS, 2002, 43 (09) :4452-4505