Speed-up via Quantum Sampling

The Markov Chain Monte Carlo method is at the heart of most fully-polynomial randomized approximation schemes for #P-complete problems such as estimating the permanent or the value of a polytope. It is therefore very natural and important to determine whether quantum computers can speed-up classical mixing processes based on Markov chains. To this end, we present a new quantum algorithm, making it possible to prepare a quantum sample, i.e., a coherent version of the stationary distribution of a reversible Markov chain. Our algorithm has a significantly better running time than that of a previous algorithm based on adiabatic state generation. We also show that our methods provide a speed-up over a recently proposed method for obtaining ground states of (classical) Hamiltonians. In an upcoming article, we will show that they yield speed-ups of classical algorithms for approximately evaluating the permanent.