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A general framework for randomized benchmarking

2021; Cambridge University Press; Linguagem: Inglês

0003-0503

Jonas Helsen, Ingo Roth, Emilio Onorati, Albert H. Werner, Jens Eisert,

Semiconductor materials and devices

The term randomized benchmarking refers to a collection of protocols that in the past decade have become the gold standard for characterizing quantum gates. These protocols aim at efficiently estimating the quality of a set of quantum gates in a way that is resistant to state preparation and measurement errors, and over the years many versions have been devised. In this work, we develop a comprehensive framework of randomized benchmarking general enough to encompass virtually all known protocols. Overcoming previous limitations on e.g. error models and gate sets, this framework allows us to formulate realistic conditions under which we can rigorously guarantee that the output of a randomized benchmarking experiment is well-described by a linear combination of matrix exponential decays. We complement this with a detailed discussion of the fitting problem associated to randomized benchmarking data. We discuss modern signal processing techniques and their guarantees in the context of randomized benchmarking, give analytical sample complexity bounds and numerically evaluate their performance and limitations. In order to reduce the resource demands of this fitting problem, we moreover provide scalable post-processing techniques to isolate exponential decays, significantly improving the practical feasibility of a large set of randomized benchmarking protocols. These post-processing techniques generalize several previously proposed methods such as character benchmarking and linear-cross entropy benchmarking. Finally we discuss, in full generality, how and when randomized benchmarking decay rates can be used to infer quality measures like the average fidelity. On the technical side, our work significantly extends the recently developed Fourier-theoretic perspective on randomized benchmarking and combines it with the perturbation theory of invariant subspaces and ideas from signal processing.