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arXiv 2102.02875quant-ph
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用变分绝热量子计算改进变分量子本征求解器

Improving the variational quantum eigensolver using variational adiabatic quantum computing

Stuart M. Harwood, Dimitar Trenev, Spencer T. Stober, Panagiotis Barkoutsos, Tanvi P. Gujarati, Sarah Mostame, Donny Greenberg

摘要:

变分量子本征求解器(VQE)是一种混合量子-经典算法,用于求解包含参数化量子线路优化的哈密顿量的最小本征值。由于所得到的优化问题一般是非凸的,该方法可以收敛到次优的参数值,不产生最小特征值。在这项工作中,我们通过采用变分绝热量子计算(VAQC)的概念来解决这个缺点,作为一个程序,以改善VQE。在VAQC中,连续参数化哈密顿量的基态通过参数化量子线路来近似。我们讨论了VAQC的一些基本理论,以促进混合量子-经典同伦延拓方法的发展。该方法与微分方程数值积分的预估-校正方法具有相似之处。虽然该过程存在理论上的限制,但我们在实践中看到VAQC可以成功地找到良好的初始电路参数来初始化VQE。我们用量子化学中的两个例子来证明这一点。通过这些例子,我们提供的经验证据表明,VAQC,结合其他技术(一个自适应终止标准的经典优化和方差为基础的reservation方法的期望值评估),可以提供更准确的解决方案比“平原”VQE,同样的努力。

英文摘要:

The variational quantum eigensolver (VQE) is a hybrid quantum-classical algorithm for finding the minimum eigenvalue of a Hamiltonian that involves the optimization of a parameterized quantum circuit. Since the resulting optimization problem is in general nonconvex, the method can converge to suboptimal parameter values which do not yield the minimum eigenvalue. In this work, we address this shortcoming by adopting the concept of variational adiabatic quantum computing (VAQC) as a procedure to improve VQE. In VAQC, the ground state of a continuously parameterized Hamiltonian is approximated via a parameterized quantum circuit. We discuss some basic theory of VAQC to motivate the development of a hybrid quantum-classical homotopy continuation method. The proposed method has parallels with a predictor-corrector method for numerical integration of differential equations. While there are theoretical limitations to the procedure, we see in practice that VAQC can successfully find good initial circuit parameters to initialize VQE. We demonstrate this with two examples from quantum chemistry. Through these examples, we provide empirical evidence that VAQC, combined with other techniques (an adaptive termination criteria for the classical optimizer and a variance-based resampling method for the expectation evaluation), can provide more accurate solutions than "plain" VQE, for the same amount of effort.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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