Quantum Monte Carlo Simulations of Tunneling in Quantum Adiabatic Optimization

TL;DR

This paper investigates whether path-integral quantum Monte Carlo methods can effectively simulate tunneling in quantum adiabatic optimization, showing numerical evidence of success in similar regimes.

Contribution

It provides a comparative analysis demonstrating that quantum Monte Carlo can replicate the tunneling behavior of quantum adiabatic algorithms under certain conditions.

Findings

Quantum Monte Carlo succeeds where quantum adiabatic optimization succeeds.

Numerical evidence supports the effectiveness of Monte Carlo in simulating tunneling.

The success depends on the properties of the potential barrier in the cost function.

Abstract

We explore to what extent path-integral quantum Monte Carlo methods can efficiently simulate the tunneling behavior of quantum adiabatic optimization algorithms. Specifically we look at symmetric cost functions defined over n bits with a single potential barrier that a successful optimization algorithm will have to tunnel through. The height and width of this barrier depend on n, and by tuning these dependencies, we can make the optimization algorithm succeed or fail in polynomial time. In this article we compare the strength of quantum adiabatic tunneling with that of path-integral quantum Monte Carlo methods. We find numerical evidence that quantum Monte Carlo algorithms will succeed in the same regimes where quantum adiabatic optimization succeeds.