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Randomized benchmarking in the quantum advantage regime.

Jordan Hines, Daniel Hothem, Marie Lu, Ravi Naik, Akel Hashim, Jean-Loup Ville, Bradley Mitchell, David Santiago, Stefan Seritan, Erik Nielsen, Robin Blume-Kohout, Kevin C Young, Irfan A. Siddiqi, K. Birgitta Whaley, Timothy J Proctor

Published Oct 1, 2022• 0 citations
Publisher
DOI
10.2172/2005742

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Recently, there has been an emergence of useful applications for noisy intermediate-scale quantum (NISQ) devices notably, though not exclusively, in the fields of quantum machine learning and variational quantum algorithms. In such applications, circuits of various depths and composed of different sets of gates are run on NISQ devices. Therefore, it is crucial to find practical ways to capture the general performance of circuits on these devices. Motivated by this pressing need, we modified the standard Clifford randomized benchmarking (RB) and interleaved RB schemes targeting them to hardware limitations. Firstly we remove the requirement for, and assumptions on, the inverse operator, in Clifford RB by incorporating a tehchnique from quantum verification. This introduces another figure of merit by which to assess the quality of the NISQ hardware, namely the acceptance probability of quantum verification. Many quantum algorithms, that provide an advantage over classical algorithms, demand the use of Clifford as well as non-Clifford gates. Therefore, as our second contribution we develop a technique for characterising a variety of non-Clifford gates, by combining tools from gate synthesis with interleaved RB. Both of our techniques are most relevant when used in conjunction with RB schemes that benchmark generators (or native gates) of the Clifford group, and in low error regimes.
Randomized benchmarking (RB) is a widely used strategy to assess the quality of available quantum gates in a computational context. RB involves applying known random sequences of gates to an initial state and using the statistics of a final measurement step to determine an effective depolarizing error per step of the sequence, which is a metric of gate quality. Here we investigate the advantages of fully randomized benchmarking, where a new random sequence is drawn for each experimental trial. The advantages of full randomization include smaller confidence intervals on the inferred step error, the ability to use maximum likelihood analysis without heuristics, straightforward optimization of the sequence lengths, and the ability to model and measure behaviors that go beyond the typical assumption of time-independent error rates. We discuss models of time-dependent or non-Markovian errors that generalize the basic RB model of a single exponential decay of the success probability. For any of these models, we implement a concrete protocol to minimize the uncertainty of the estimated parameters given a fixed time constraint on the complete experiment, and we implement a maximum likelihood analysis. We consider several previously published experiments and determine the potential for improvements with optimized full randomization. We experimentally observe such improvements in Clifford randomized benchmarking experiments on a single trapped ion qubit at the National Institute of Standards and Technology (NIST). For an experiment with uniform lengths and intentionally repeated sequences the step error was 2.42−0.22+0.30×10−52.42^{+0.30}_{-0.22}\times 10^{-5}2.42−0.22+0.30​×10−5, and for an optimized fully randomized experiment of the same total duration the step error was 2.57−0.06+0.07×10−52.57^{+0.07}_{-0.06}\times 10^{-5}2.57−0.06+0.07​×10−5. We find a substantial decrease in the uncertainty of the step error as a result of optimized fully randomized benchmarking.
Ternary quantum processors offer significant potential computational advantages over conventional qubit technologies, leveraging the encoding and processing of quantum information in qutrits (three-level systems). To evaluate and compare the performance of such emerging quantum hardware it is essential to have robust benchmarking methods suitable for a higher-dimensional Hilbert space. We demonstrate extensions of industry standard randomized benchmarking (RB) protocols, developed and used extensively for qubits, suitable for ternary quantum logic. Using a superconducting five-qutrit processor, we find an average single-qutrit process infidelity of 3.8×10^{-3}. Through interleaved RB, we characterize a few relevant gates, and employ simultaneous RB to fully characterize crosstalk errors. Finally, we apply cycle benchmarking to a two-qutrit CSUM gate and obtain a two-qutrit process fidelity of 0.85. Our results present and demonstrate RB-based tools to characterize the performance of a qutrit processor, and a general approach to diagnose control errors in future qudit hardware.