Accelerated randomized benchmarking

Abstract

Quantum information processing offers promising advances for a wide range of fields and applications, provided that we can efficiently assess the performance of the control applied in candidate systems. That is, we must be able to determine whether we have implemented a desired gate, and refine accordingly. Randomized benchmarking reduces the difficulty of this task by exploiting symmetries in quantum operations. Here, we bound the resources required for benchmarking and show that, with prior information, we can achieve several orders of magnitude better accuracy than in traditional approaches to benchmarking. Moreover, by building on state-of-the-art classical algorithms, we reach these accuracies with near-optimal resources. Our approach requires an order of magnitude less data to achieve the same accuracies and to provide online estimates of the errors in the reported fidelities. We also show that our approach is useful for physical devices by comparing to simulations.Quantum information processing devices offer great promise in a variety of different fields, including chemistry and material science, data analysis and machine learning [1][2][3][4], as well as cryptography [5]. Over the past few years, proposals have been advanced for quantum information processing past the classical scale, based on nodebased architectures [6,7]. In addition, rapid progress has been made towards experimental implementations that might allow for developing such devices [8,9]. An impediment in this effort, however, is presented by the difficulty of calibrating and diagnosing quantum devices.In particular, in the development of quantum information processing, an important experimental challenge is to efficiently characterize the quality with which we can control a quantum system. By characterizing the quality of a quantum gate that is implemented by a control pulse, we can then reason about the utility of that gate for quantum information processing tasks. For instance, we can estimate the feasibility of and the resources required to implement error correction using that control by comparing to proven and numerically estimated fault-tolerance thresholds [10,11]. Alternately, we can adjust our control sequences to account for differences between our control model and the actual system.In cases where only the quality of a quantum gate or set of gates is required, randomized benchmarking has proven to be a useful means of extracting this information with relatively little experimental effort [12]. This has been demonstrated in a variety of experimental settings [9,[13][14][15][16][17][18][19]. Randomized benchmarking has also been used to improve gate fidelities by characterizing cross-talk [20] or distortions [21]. Extracting fidelity information can often be useful in diagnosing performance and problems with a device in lieu of full characterization [22]. Moreover, randomized benchmarking has also been used to extract information about the completely positive and unital parts of linear maps [23].Here, using near-optimal data proces...

Robust characterization of leakage errors

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