Random Circuit Metrics for Performance Assessment and Model Testing

Random circuit metrics, such as cross-entropy benchmarking, have recently emerged as a powerful set of tools to assess the performance of quantum processors. A natural question is to what extent these techniques can be used to learn noise characteristics of the processor. Here, we present results on extensions of random circuit metrics to testing error models. We demonstrate for small processors built from superconducting qubits that analysis of random circuit distributions is a viable method to compare candidate error models for device operation. Such models feed directly into the debugging cycle, and can be used to guide future operation towards optimal performance, or in the design of future devices.

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