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arXiv 2105.08086quant-phcs.AIcs.LG
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近项量子模拟的神经误差抑制

Neural Error Mitigation of Near-Term Quantum Simulations

QB Information Technologies (1QBit)(1QB信息技术公司(1QBit));University of Waterloo(滑铁卢大学);Perimeter Institute for Theoretical Physics(圆周理论物理研究所);Vector Institute(向量研究所);Institute for Quantum Computing, University of Waterloo(滑铁卢大学量子计算研究所)

Elizabeth R. Bennewitz, Florian Hopfmueller, Bohdan Kulchytskyy, Juan Carrasquilla, Pooya Ronagh

摘要:

近期量子计算机为寻找量子系统的基态提供了一个很有前途的平台,这是物理、化学和材料科学的一项重要任务。然而,短期方法受到噪声影响以及短期量子硬件资源有限的限制。我们介绍了“神经误差缓解”,它使用神经网络,以提高估计的基态和基态观测值获得使用近期量子模拟。为了证明我们的方法的广泛适用性,我们采用神经误差缓解找到基态的H $_2$和LiH分子哈密顿,以及晶格Schwinger模型,通过变分量子本征求解器(VQE)。我们的研究结果表明,神经误差缓解改善了数值和实验VQE计算,以产生低能量误差,高精度和更复杂的观测值,如序参量和纠缠熵的准确估计,而不需要额外的量子资源。此外,神经误差缓解对于所使用的量子态准备算法、其上实现的量子硬件以及影响实验的特定噪声信道是不可知的,这有助于其作为量子模拟的工具的多功能性。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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