An optimal quantum sampling regression algorithm for variational eigensolving in the low qubit number regime

Pedro Rivero, I. Cloet, Z. Sullivan
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引用次数: 0

Abstract

The VQE algorithm has turned out to be quite expensive to run given the way we currently access quantum processors (i.e. over the cloud). In order to alleviate this issue, we introduce Quantum Sampling Regression (QSR), an alternative hybrid quantum-classical algorithm, and analyze some of its use cases based on time complexity in the low qubit number regime. In exchange for some extra classical resources, this novel strategy is proved to be optimal in terms of the number of samples it requires from the quantum processor. We develop a simple analytical model to evaluate when this algorithm is more efficient than VQE, and, from the same theoretical considerations, establish a threshold above which quantum advantage can occur. Finally, we demonstrate the efficacy of our algorithm for a benchmark problem.
低量子位数下变分特征解的最优量子抽样回归算法
考虑到我们目前访问量子处理器的方式(即通过云),VQE算法的运行成本相当高。为了缓解这一问题,我们引入了量子采样回归(QSR)算法,这是一种替代的量子-经典混合算法,并分析了它在低量子比特数下基于时间复杂度的一些用例。为了换取一些额外的经典资源,这种新策略被证明在量子处理器所需的样本数量方面是最优的。我们开发了一个简单的分析模型来评估该算法何时比VQE更有效,并且从相同的理论考虑出发,建立了量子优势可能发生的阈值。最后,我们证明了我们的算法对一个基准问题的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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