论文标题
部分可观测时空混沌系统的无模型预测
Differentiable matrix product states for simulating variational quantum computational chemistry
论文作者
论文摘要
量子计算被认为是量子化学问题的最终解决方案。在大规模，完全容忍的量子计算机出现之前，变异量子本元素〜（VQE）是一种有前途的启发式量子算法，可在近期噪声量子计算机上解决现实世界中的量子化学问题。在这里，我们根据量子状态的矩阵乘积状态表示，为VQE提出了一个高度可行的经典模拟器，该模拟器大大扩展了现有模拟器的模拟范围。我们的模拟器将量子电路演变无缝地集成到经典的自动差异框架中，因此可以有效地计算梯度与经典的深神经网络有效地相似，其缩放与变量参数的数量无关。作为应用程序，我们使用模拟器研究常用的小分子，例如HF，HCL，LIH和H $ _2 $ O，以及较大的分子Co $ _2 $，BEH $ _2 $和H $ _4 $，最多$ 40 $ Qubits。我们的模拟器对量子数的数量的有利缩放和参数的数量可以使其成为近期量子算法的理想测试场，并且是噪声量子计算机上大规模VQE实验的完美基准基线。
Quantum Computing is believed to be the ultimate solution for quantum chemistry problems. Before the advent of large-scale, fully fault-tolerant quantum computers, the variational quantum eigensolver~(VQE) is a promising heuristic quantum algorithm to solve real world quantum chemistry problems on near-term noisy quantum computers. Here we propose a highly parallelizable classical simulator for VQE based on the matrix product state representation of quantum state, which significantly extend the simulation range of the existing simulators. Our simulator seamlessly integrates the quantum circuit evolution into the classical auto-differentiation framework, thus the gradients could be computed efficiently similar to the classical deep neural network, with a scaling that is independent of the number of variational parameters. As applications, we use our simulator to study commonly used small molecules such as HF, HCl, LiH and H$_2$O, as well as larger molecules CO$_2$, BeH$_2$ and H$_4$ with up to $40$ qubits. The favorable scaling of our simulator against the number of qubits and the number of parameters could make it an ideal testing ground for near-term quantum algorithms and a perfect benchmarking baseline for oncoming large scale VQE experiments on noisy quantum computers.
