Quantum-assisted Monte Carlo algorithms for fermions

Quantum computing is a promising way to systematically solve the longstanding computational problem, ground state of many-body fermion system. Many efforts have been made realise certain forms quantum advantage in this for instance, development variational algorithms. A recent work by Huggins et al. [1] reports novel candidate, i.e. quantum-classical hybrid Monte Carlo algorithm with reduced bias comparison its fully-classical counterpart. In paper, we propose family scalable quantum-assisted algorithms where computer used at minimal cost and still can reduce bias. By incorporating Bayesian inference approach, achieve quantum-facilitated reduction much smaller quantum-computing than taking empirical mean amplitude estimation. Besides, show that framework general suppress errors obtained from classical Our provides toolkit achieving quantum-enhanced calculation systems on near-term devices.