论文标题
部分可观测时空混沌系统的无模型预测
Adaptive pruning-based optimization of parameterized quantum circuits
论文作者
论文摘要
Variational hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices.尽管过去的研究发展了强大而表现力的Ansatze，但它们的近期应用受到优化在广泛的参数空间中的困难而受到限制。在这项工作中，我们提出了一种用于各种量子算法中使用的ANSATZE的启发式优化策略，我们称之为“参数有效的电路训练”（PECT）。 PECT并没有立即优化所有ANSATZ参数，而是启动了一系列变异算法，其中算法的每次迭代都激活并优化了总参数集的子集。 To update the parameter subset between iterations, we adapt the dynamic sparse reparameterization scheme by Mostafa et al. (arXiv:1902.05967).我们展示了变异量子本质量的PECT，其中我们基准的单一耦合群集Ansatze（包括UCCSD和K-UPCCGSD）以及低深度回路ANSATZ（LDCA），以估计分子系统的基态基能。另外，我们还使用PECT的图层变体来优化助理处理器的硬件效率电路，以估计一维费米 - 哈伯德模型的基态能量密度。从我们的数值数据中，我们发现PECT可以优化某些以前难以收敛的Ansatze，并且通常可以通过减少编码解决方案候选者的优化运行时和/或电路深度来改善变量算法的性能。
Variational hybrid quantum-classical algorithms are powerful tools to maximize the use of Noisy Intermediate Scale Quantum devices. While past studies have developed powerful and expressive ansatze, their near-term applications have been limited by the difficulty of optimizing in the vast parameter space. In this work, we propose a heuristic optimization strategy for such ansatze used in variational quantum algorithms, which we call "Parameter-Efficient Circuit Training" (PECT). Instead of optimizing all of the ansatz parameters at once, PECT launches a sequence of variational algorithms, in which each iteration of the algorithm activates and optimizes a subset of the total parameter set. To update the parameter subset between iterations, we adapt the dynamic sparse reparameterization scheme by Mostafa et al. (arXiv:1902.05967). We demonstrate PECT for the Variational Quantum Eigensolver, in which we benchmark unitary coupled-cluster ansatze including UCCSD and k-UpCCGSD, as well as the low-depth circuit ansatz (LDCA), to estimate ground state energies of molecular systems. We additionally use a layerwise variant of PECT to optimize a hardware-efficient circuit for the Sycamore processor to estimate the ground state energy densities of the one-dimensional Fermi-Hubbard model. From our numerical data, we find that PECT can enable optimizations of certain ansatze that were previously difficult to converge and more generally can improve the performance of variational algorithms by reducing the optimization runtime and/or the depth of circuits that encode the solution candidate(s).
