Quantum-assisted Monte Carlo algorithms for fermions

Xiaosi Xu and Ying Li

Graduate School of China Academy of Engineering Physics, Beijing 100193, China

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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. [1] 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.

Solving the Schrodinger equation of many-body fermion systems is essential in many scientific fields. Quantum Monte Carlo (QMC) is a group of well-developed classical algorithms that have been widely used. However, a sign problem prohibits its use for large systems as the variance of the results increases exponentially with the system size. Common methods to constrain the sign problem usually introduces some bias. We consider incorporating quantum computers into QMC to reduce the bias. Prior works have some issues with scalability in general and quantum computation cost. In this work, we try to address the issues and introduce a framework of quantum-assisted QMC algorithms where the quantum computer is involved at flexible levels. We describe two strategies based on the extent of quantum resources used and show notably improved numerical results compared with the classical counterpart. To further reduce the quantum computing measurements, we introduce a Bayesian inference method and show that a stable quantum advantage can be maintained. With inherent symmetry in the target physical system, our quantum-assisted QMC is resilient to errors. By making our quantum-assisted QMC a subroutine of the subspace diagonalization algorithm, we show that quantum-assisted QMC is a general method of reducing errors in other classical or quantum algorithms. The quantum-assisted QMC is a potentially new method to demonstrate some level of quantum advantage on NIST machines.

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