论文标题
量子限制的鲍尔茨曼机器通用量子计算
Quantum restricted Boltzmann machine universal for quantum computation
论文作者
论文摘要
量子物理学中的多体问题提出的挑战源于描述具有高复杂性的多体波函数中编码的非平凡相关性。量子神经网络提供了一种强大的工具来表示大规模波函数，这引起了量子优势时代的广泛关注。一个重要的开放问题是单层量子神经网络的代表力边界的确切问题。在本文中，我们设计了一个2局的哈密顿量，然后给出了一种基于它的量子限制的玻尔兹曼机器（QRBM，即单层量子神经网络）。提出的QRBM具有以下两个显着特征。 （1）事实证明，实施量子计算任务是普遍的。 （2）它可以在嘈杂的中间量子量子（NISQ）设备上有效实现。我们成功利用所提出的QRBM来计算著名的物理感兴趣案例的波函数，包括基态以及分子的Gibbs状态（热状态）在超导量子芯片上。实验结果说明了提出的QRBM可以以可接受的误差计算上述波函数。
The challenge posed by the many-body problem in quantum physics originates from the difficulty of describing the nontrivial correlations encoded in the many-body wave functions with high complexity. Quantum neural network provides a powerful tool to represent the large-scale wave function, which has aroused widespread concern in the quantum superiority era. A significant open problem is what exactly the representational power boundary of the single-layer quantum neural network is. In this paper, we design a 2-local Hamiltonian and then give a kind of Quantum Restricted Boltzmann Machine (QRBM, i.e. single-layer quantum neural network) based on it. The proposed QRBM has the following two salient features. (1) It is proved universal for implementing quantum computation tasks. (2) It can be efficiently implemented on the Noisy Intermediate-Scale Quantum (NISQ) devices. We successfully utilize the proposed QRBM to compute the wave functions for the notable cases of physical interest including the ground state as well as the Gibbs state (thermal state) of molecules on the superconducting quantum chip. The experimental results illustrate the proposed QRBM can compute the above wave functions with an acceptable error.
