Near-term quantum computers provide a promising platform for finding ground states of quantum systems, which is an essential task in physics, chemistry, and materials science. Near-term approaches, however, are constrained by the effects of noise as well as the limited resources of near-term quantum hardware. We introduce "neural error mitigation," which uses neural networks to improve estimates of ground states and ground-state observables obtained using near-term quantum simulations. To demonstrate our method's broad applicability, we employ neural error mitigation to find the ground states of the H$_2$ and LiH molecular Hamiltonians, as well as the lattice Schwinger model, prepared via the variational quantum eigensolver (VQE). Our results show that neural error mitigation improves numerical and experimental VQE computations to yield low energy errors, high fidelities, and accurate estimations of more-complex observables like order parameters and entanglement entropy, without requiring additional quantum resources. Furthermore, neural error mitigation is agnostic with respect to the quantum state preparation algorithm used, the quantum hardware it is implemented on, and the particular noise channel affecting the experiment, contributing to its versatility as a tool for quantum simulation.
翻译:近距离量子计算机为寻找量子系统的地面状态提供了一个充满希望的平台,这是物理、化学和材料科学中的一项基本任务。然而,近距离方法受到噪音的影响以及近期量子硬件的有限资源的限制。我们引入了“神经错误减缓”, 使用神经网络来改进对地面状态和地面状态的估算, 利用近期量子模拟获得的地面状态和地面状态观测的准确估算。 为了展示我们的方法的广泛适用性, 我们使用神经错误缓解, 以找到H$_2$和LiH分子汉密尔顿仪的地面状态, 以及通过变异量量量量子离子仪(VQE)制作的lattice Schwinger模型。 我们的结果表明, 神经错误缓解改善了数值和实验性VQE计算, 以产生较低的能源误差, 高度忠诚, 并准确估计了更复杂的可观察到的顺序参数和缠绕的酶, 而不需要额外的量子资源。此外, 神经错误缓解是对于使用量子状态准备算法、 量子硬件硬件和特定反射道实验的贡献。