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Stochastic gradient line Bayesian optimization for efficient noise-robust optimization of parameterized quantum circuits
npj Quantum Information ( IF 7.6 ) Pub Date : 2022-07-27 , DOI: 10.1038/s41534-022-00592-6
Shiro Tamiya, Hayata Yamasaki

Optimizing parameterized quantum circuits is a key routine in using near-term quantum devices. However, the existing algorithms for such optimization require an excessive number of quantum-measurement shots for estimating expectation values of observables and repeating many iterations, whose cost has been a critical obstacle for practical use. We develop an efficient alternative optimization algorithm, stochastic gradient line Bayesian optimization (SGLBO), to address this problem. SGLBO reduces the measurement-shot cost by estimating an appropriate direction of updating circuit parameters based on stochastic gradient descent (SGD) and further utilizing Bayesian optimization (BO) to estimate the optimal step size for each iteration in SGD. In addition, we formulate an adaptive measurement-shot strategy and introduce a technique of suffix averaging to reduce the effect of statistical and hardware noise. Our numerical simulation demonstrates that the SGLBO augmented with these techniques can drastically reduce the measurement-shot cost, improve the accuracy, and make the optimization noise-robust.



中文翻译:

随机梯度线贝叶斯优化,用于参数化量子电路的有效抗噪优化

优化参数化量子电路是使用近期量子器件的关键例程。然而,现有的这种优化算法需要大量的量子测量镜头来估计可观测物的期望值并重复多次迭代,其成本一直是实际应用的关键障碍。我们开发了一种有效的替代优化算法,随机梯度线贝叶斯优化(SGLBO)来解决这个问题。SGLBO 通过基于随机梯度下降 (SGD) 估计更新电路参数的适当方向并进一步利用贝叶斯优化 (BO) 来估计 SGD 中每次迭代的最佳步长,从而降低了测量镜头成本。此外,我们制定了一种自适应测量镜头策略,并引入了一种后缀平均技术,以减少统计和硬件噪声的影响。我们的数值模拟表明,使用这些技术增强的 SGLBO 可以显着降低测量成本,提高精度,并使优化噪声鲁棒。

更新日期:2022-07-27
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