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Efficient Quantum State Sample Tomography with Basis-Dependent Neural Networks

Smith, Alistair W. R. and Gray, Johnnie and Kim, M. S. (2021) Efficient Quantum State Sample Tomography with Basis-Dependent Neural Networks. PRX Quantum, 2 (2). Art. No. 020348. ISSN 2691-3399. doi:10.1103/prxquantum.2.020348.

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We use a metalearning neural-network approach to analyze data from a measured quantum state. Once our neural network has been trained, it can be used to efficiently sample measurements of the state in measurement bases not contained in the training data. These samples can be used to calculate expectation values and other useful quantities. We refer to this process as “state sample tomography.” We encode the state’s measurement outcome distributions using an efficiently parameterized generative neural network. This allows each stage in the tomography process to be performed efficiently even for large systems. Our scheme is demonstrated on recent IBM Quantum devices, producing a model for a six-qubit state’s measurement outcomes with a predictive accuracy (classical fidelity) greater than 95% for all test cases using only 100 random measurement settings as opposed to the 729 settings required for standard full tomography using local measurements. This reduction in the required number of measurements scales favorably, with training data in 200 measurement settings, yielding a predictive accuracy greater than 92% for a ten-qubit state where 59049 settings are typically required for full local measurement-based quantum state tomography. A reduction in the number of measurements by a factor, in this case, of almost 600 could allow for estimations of expectation values and state fidelities in practicable times on current quantum devices.

Item Type:Article
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URLURL TypeDescription Paper
Smith, Alistair W. R.0000-0003-1047-726X
Gray, Johnnie0000-0001-9461-3024
Alternate Title:Efficient Approximate Quantum State Tomography with Basis Dependent Neural-Networks
Additional Information:© 2021 The Author(s). Published by the American Physical Society under the terms of the Creative Commons Attribution 4.0 International license. Further distribution of this work must maintain attribution to the author(s) and the published article's title, journal citation, and DOI. (Received 18 August 2020; accepted 1 June 2021; published 28 June 2021) This work is supported by the UK EPSRC (EP/P510257/1), the EPSRC Hub in Quantum Computing and Simulation (EP/T001062/1), the Royal Society, and the Samsung GRP grant. We acknowledge the use of IBM Quantum Services for this work. The views expressed are those of the authors, and do not reflect the official policy or position of IBM or the IBM Quantum team.
Funding AgencyGrant Number
Engineering and Physical Sciences Research Council (EPSRC)EP/P510257/1
Engineering and Physical Sciences Research Council (EPSRC)EP/T001062/1
Issue or Number:2
Record Number:CaltechAUTHORS:20210628-220423633
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109636
Deposited By: George Porter
Deposited On:29 Jun 2021 14:51
Last Modified:16 Nov 2021 19:37

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