Quantum computers have a potential for solving quantum chemistry problems with higher accuracy than classical computers. Quantum computing quantum Monte Carlo (QC-QMC) is a QMC with a trial state prepared in quantum circuit, which is employed to obtain the ground state with higher accuracy than QMC alone. We propose an algorithm combining QC-QMC with a hybrid tensor network to extend the applicability of QC-QMC beyond a single quantum device size. In a two-layer quantum-quantum tree tensor, our algorithm for the larger trial wave function can be executed than preparable wave function in a device. Our algorithm is evaluated on the Heisenberg chain model, graphite-based Hubbard model, hydrogen plane model, and MonoArylBiImidazole using full configuration interaction QMC. Our algorithm can achieve energy accuracy (specifically, variance) several orders of magnitude higher than QMC, and the hybrid tensor version of QMC gives the same energy accuracy as QC-QMC when the system is appropriately decomposed. Moreover, we develop a pseudo-Hadamard test technique that enables efficient overlap calculations between a trial wave function and an orthonormal basis state. In a real device experiment by using the technique, we obtained almost the same accuracy as the statevector simulator, indicating the noise robustness of our algorithm. These results suggests that the present approach will pave the way to electronic structure calculation for large systems with high accuracy on current quantum devices.

量子计算机有潜力以比经典计算机更高的精度解决量子化学问题。量子计算量子蒙特卡罗（QC-QMC）是一种在量子电路中准备的带有试态的QMC，用于获得比单独QMC更高精度的基态。我们提出了一种将 QC-QMC 与混合张量网络相结合的算法，以将 QC-QMC 的适用性扩展到单个量子设备尺寸之外。在两层量子-量子树张量中，我们针对更大的试验波函数的算法可以比设备中可准备的波函数执行。我们的算法使用全构型相互作用 QMC 在海森堡链模型、基于石墨的哈伯德模型、氢平面模型和单芳基双咪唑上进行了评估。我们的算法可以实现比 QMC 高几个数量级的能量精度（具体来说，方差），并且当系统适当分解时，QMC 的混合张量版本给出与 QC-QMC 相同的能量精度。此外，我们开发了一种伪哈达玛测试技术，可以实现试验波函数和正交基态之间的有效重叠计算。在使用该技术的真实设备实验中，我们获得了与状态向量模拟器几乎相同的精度，表明我们的算法具有噪声鲁棒性。这些结果表明，本方法将为当前量子器件上高精度大型系统的电子结构计算铺平道路。