Systematic improvement of neural network quantum states using a Lanczos recursion

Understand

The quantum many-body problem lies at the center of the most important open challenges in condensed matter, quantum chemistry, atomic, nuclear, and high-energy physics.

• While quantum Monte Carlo, when applicable, remains the most powerful numerical technique capable of treating dozens or hundreds of degrees of freedom with high accuracy, it is restricted to models that are not afflicted by the infamous sign problem.
• A powerful alternative that has emerged in recent years is the use of neural networks as variational estimators for quantum states.
• In this work, we propose a symmetry-projected variational solution in the form of linear combinations of simple restricted Boltzmann machines.