Variational Block Correlated Coupled Cluster Monte Carlo
Motivated by the recent success of quantum neural states, we present a variational implementation of the coupled cluster method suitable for quantum Monte Carlo simulations. We introduce a Young tableaux representation of the wave-function coefficients and their derivatives that substantially reduces the computational complexity of the variational optimization. While the algorithm is generic, we illustrate its implementation with an application to the Heisenberg model in one and two dimensions, using the block correlated coupled cluster ansatz (BCCC) with doubles and triples. Convergence is greatly improved by means of importance sampling based on a fictitious temperature. The resulting approach achieves competitive accuracy while providing a scalable framework for variational coupled-cluster calculations.