Probabilistic Computing for Quantum Simulations

This paper addresses the computational bottleneck in simulating quantum many-body systems using neural networks. By combining sparse Boltzmann machines with probabilistic computing hardware (FPGAs), the authors achieve significant improvements in scaling and efficiency. The use of a custom multi-FPGA cluster and a novel dual-sampling algorithm for training deep Boltzmann machines are key contributions, enabling simulations of larger systems and deeper variational architectures. This work is significant because it offers a potential path to overcome the limitations of traditional Monte Carlo methods in quantum simulations.