论文标题
低Quit数量方案中变异本征量的最佳量子抽样回归算法
An optimal quantum sampling regression algorithm for variational eigensolving in the low qubit number regime
论文作者
论文摘要
鉴于我们当前访问量子处理器的方式（即在云上），VQE算法的运行非常昂贵。为了减轻此问题，我们引入了量子采样回归（QSR），这是一种替代的混合量子古典算法，并基于低量子数数制度的时间复杂性来分析其某些用例。为了换取一些额外的古典资源，就量子处理器所需的样本数量而言，这种新颖的策略被证明是最佳的。我们开发了一个简单的分析模型，以评估该算法何时比VQE更有效，并且从相同的理论考虑上，建立一个阈值，在该阈值中可能发生量子优势。最后，我们证明了算法对基准问题的功效。
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.
