Randomized compiling for scalable quantum computing on a noisy superconducting quantum processor

TL;DR

This paper demonstrates that randomized compiling effectively converts coherent errors into stochastic noise, significantly improving the performance and predictability of quantum algorithms on noisy superconducting quantum processors.

Contribution

It provides experimental validation that randomized compiling enhances quantum algorithm performance and enables accurate error-based predictions on NISQ devices.

Findings

Performance gains in quantum Fourier transform and random circuits

Accurate prediction of algorithm performance from error rates

Validation of randomized compiling as a scalable error mitigation technique

Abstract

The successful implementation of algorithms on quantum processors relies on the accurate control of quantum bits (qubits) to perform logic gate operations. In this era of noisy intermediate-scale quantum (NISQ) computing, systematic miscalibrations, drift, and crosstalk in the control of qubits can lead to a coherent form of error which has no classical analog. Coherent errors severely limit the performance of quantum algorithms in an unpredictable manner, and mitigating their impact is necessary for realizing reliable quantum computations. Moreover, the average error rates measured by randomized benchmarking and related protocols are not sensitive to the full impact of coherent errors, and therefore do not reliably predict the global performance of quantum algorithms, leaving us unprepared to validate the accuracy of future large-scale quantum computations. Randomized compiling is a protocol designed to overcome these performance limitations by converting coherent errors into stochastic noise, dramatically reducing unpredictable errors in quantum algorithms and enabling accurate predictions of algorithmic performance from error rates measured via cycle benchmarking. In this work, we demonstrate significant performance gains under randomized compiling for the four-qubit quantum Fourier transform algorithm and for random circuits of variable depth on a superconducting quantum processor. Additionally, we accurately predict algorithm performance using experimentally-measured error rates. Our results demonstrate that randomized compiling can be utilized to leverage and predict the capabilities of modern-day noisy quantum processors, paving the way forward for scalable quantum computing.