论文标题
使用经典优化的变分量子质量识别拓扑阶段
Identification of topological phases using classically-optimized variational quantum eigensolver
论文作者
论文摘要
变异量子本素（VQE）被认为是近期量子计算机的混合量子量子算法的有前途的候选者。同时，VQE面临的挑战是，与测量相关的统计误差以及系统误差可能会显着妨碍优化。为了解决这个问题，我们提出了经典优化的VQE（Co-VQE），其中优化的整个过程都在经典的计算机上有效地进行。通过观察到，具有恒定（或对数）深度的量子电路可以通过局部子系统的仿真来保证该方法的功效。在Co-VQE中，我们仅使用量子计算机在优化参数后测量非置换量。作为概念验证，我们对具有拓扑相的量子自旋模型进行了数值实验。优化后，我们通过非本地阶参数来识别拓扑阶段，以及对量子状态之间内部产物的无监督机器学习。所提出的方法最大程度地利用了使用量子计算机的优势，同时避免在嘈杂的量子设备上进行剧烈的优化。此外，就量子机学习而言，我们的研究显示了一种有趣的方法，该方法采用量子计算机来生成量子系统的数据，同时使用经典计算机进行学习过程。
Variational quantum eigensolver (VQE) is regarded as a promising candidate of hybrid quantum-classical algorithm for the near-term quantum computers. Meanwhile, VQE is confronted with a challenge that statistical error associated with the measurement as well as systematic error could significantly hamper the optimization. To circumvent this issue, we propose classically-optimized VQE (co-VQE), where the whole process of the optimization is efficiently conducted on a classical computer. The efficacy of the method is guaranteed by the observation that quantum circuits with a constant (or logarithmic) depth are classically tractable via simulations of local subsystems. In co-VQE, we only use quantum computers to measure nonlocal quantities after the parameters are optimized. As proof-of-concepts, we present numerical experiments on quantum spin models with topological phases. After the optimization, we identify the topological phases by nonlocal order parameters as well as unsupervised machine learning on inner products between quantum states. The proposed method maximizes the advantage of using quantum computers while avoiding strenuous optimization on noisy quantum devices. Furthermore, in terms of quantum machine learning, our study shows an intriguing approach that employs quantum computers to generate data of quantum systems while using classical computers for the learning process.
