论文标题
量子辅助蒙特卡洛算法
Quantum-assisted Monte Carlo algorithms for fermions
论文作者
论文摘要
量子计算是系统地解决长期计算问题的一种有希望的方法，即多体效率系统的基础状态。在此问题中，已经做出了许多努力来实现某些形式的量子优势，例如，变异量子算法的发展。 Huggins等人的最新作品。报道了一种新颖的候选者，即一种量子型混合蒙特卡洛算法，与其完全经典的对应物相比，偏差降低。在本文中，我们提出了一个可扩展的量子辅助蒙特卡洛算法的家族，其中量子计算机以最低的成本使用，但仍然可以减少偏见。通过融合贝叶斯推理方法，我们可以实现这种量子相关的偏置减少，而量子计算的成本要比在振幅估计中进行经验平均值要小得多。此外，我们表明，混合蒙特卡洛框架是抑制从经典算法获得的基态误差的一般方法。我们的工作提供了一个蒙特卡洛工具包，用于实现近期量子设备上费米的量子增强计算。
Quantum computing is a promising way to systematically solve the longstanding computational problem, the ground state of a many-body fermion system. Many efforts have been made to realise certain forms of quantum advantage in this problem, for instance, the development of variational quantum algorithms. A recent work by Huggins et al. reports a novel candidate, i.e. a quantum-classical hybrid Monte Carlo algorithm with a reduced bias in comparison to its fully-classical counterpart. In this paper, we propose a family of scalable quantum-assisted Monte Carlo algorithms where the quantum computer is used at its minimal cost and still can reduce the bias. By incorporating a Bayesian inference approach, we can achieve this quantum-facilitated bias reduction with a much smaller quantum-computing cost than taking empirical mean in amplitude estimation. Besides, we show that the hybrid Monte Carlo framework is a general way to suppress errors in the ground state obtained from classical algorithms. Our work provides a Monte Carlo toolkit for achieving quantum-enhanced calculation of fermion systems on near-term quantum devices.
