Algorithms

Quantum-enhanced proposal distributions, defined by parameterized unitaries, showed potential for outperforming classical strategies in proposing effective moves in Monte Carlo Markov chains (MCMC). However, it is crucial to carefully tune the parameters defining these distributions, as they determine the resulting advantage over the classical counterpart. In the present work, we propose a general optimisation algorithm that exploits estimates of the autocorrelation function of a certain observable to optimise the parameters defining the proposal distributions. A Python simulator of the first version of the algorithm is available on my GitHub.

Supervisors: Prof. Crispin Barnes, Prof. Giuseppe Carleo, Dr. Hugo V. Lepage.

Colleagues: Chris Long