TL;DR
This paper introduces an accelerated randomized benchmarking method that significantly reduces data requirements and improves accuracy for assessing quantum control fidelities, enabling more efficient quantum system evaluation.
Contribution
It presents a novel approach that combines prior information and advanced algorithms to enhance randomized benchmarking efficiency and accuracy in quantum systems.
Findings
Achieves several orders of magnitude better accuracy with less data.
Requires an order of magnitude less data for the same fidelity estimates.
Enables online error estimation and practical application in physical devices.
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.…
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