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 that 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.

中文翻译：

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噪声超导量子处理器上可扩展量子计算的随机编译

在量子处理器上成功实现算法依赖于对量子位（qubits）的精确控制来执行逻辑门操作。在这个嘈杂的中尺度量子 (NISQ) 计算时代，量子位控制中的系统校准错误、漂移和串扰可能导致出现经典模拟所没有的相干形式的错误。相干错误以不可预测的方式严重限制了量子算法的性能，减轻它们的影响对于实现可靠的量子计算是必要的。此外，随机基准测试和相关协议测量的平均错误率对相干错误的全部影响不敏感，因此不能可靠地预测量子算法的全局性能，让我们没有准备好验证未来大规模量子计算的准确性。随机编译是一种协议，旨在通过将相干误差转换为随机噪声来克服这些性能限制，显着减少量子算法中的不可预测误差，并通过循环基准测试测量的误差率准确预测算法性能。在这项工作中，我们展示了在随机编译下对四量子位量子傅立叶变换算法和超导量子处理器上可变深度的随机电路的显着性能提升。此外，我们使用实验测量的错误率准确预测算法性能。