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Recovering quantum gates from few average gate fidelities

Published

Author(s)

Yi-Kai Liu, Ingo Roth, Richard Kueng, Shelby Kimmel, David Gross, Jens Eisert, Martin Kliesch

Abstract

Characterizing quantum processes is a key task in and constitutes a challenge for the development of quantum technologies, especially at the noisy intermediate scale of today's devices. One method for characterizing processes is randomized benchmarking, which is robust against state preparation and measurement (SPAM) errors, and can be used to benchmark Clifford gates. A complementing approach asks for full tomographic knowledge. Compressed sensing techniques achieve full tomography of quantum channels essentially at optimal resource efficiency. So far, guarantees for compressed sensing protocols rely on unstructured random measurements and can not be applied to the data acquired from randomized benchmarking experiments. It has been an open question whether or not the favorable features of both worlds can be combined. In this work, we give a positive answer to this question. For the important case of characterizing multi-qubit unitary gates, we provide a rigorously guaranteed and practical reconstruction method that works with an essentially optimal number of average gate fidelities measured respect to random Clifford unitaries. Moreover, for general unital quantum channels we provide an explicit expansion into a unitary 2-design, allowing for a practical and guaranteed reconstruction also in that case. As a side result, we obtain a new statistical interpretation of the unitarity -- a figure of merit that characterizes the coherence of a process. In our proofs we exploit recent representation theoretic insights on the Clifford group, develop a version of Collins' calculus with Weingarten functions for integration over the Clifford group, and combine this with proof techniques from compressed sensing.
Citation
Physical Review Letters
Volume
121

Keywords

Quantum process tomography, compressed sensing, randomized benchmarking
Created October 24, 2018, Updated July 29, 2019