Boltzmann machine learning with a variational quantum algorithm

Understand

Boltzmann machine is a powerful tool for modeling probability distributions that govern the training data.

• A thermal equilibrium state is typically used for Boltzmann machine learning to obtain a suitable probability distribution.
• The Boltzmann machine learning consists of calculating the gradient of the loss function given in terms of the thermal average, which is the most time consuming procedure.
• Here, we propose a method to implement the Boltzmann machine learning by using Noisy Intermediate-Scale Quantum (NISQ) devices.