Geometry and symmetry in quantum Boltzmann machine

The quantum Boltzmann machine extends the classical Boltzmann machine learning to the quantum regime, which makes its power to simulate the quantum states beyond the classical probability distributions. We apply the Broyden-Fletcher-Goldfarb-Shanno algorithm to study the corresponding optimization problem in quantum Boltzmann machine especially focus on the target states being a family of states with parameters. As a typical example, we study the target states being the real symmetric two-qubit pure states, and we find two obvious features shown in the numerical results on the minimal quantum relative entropy: First, the minimal quantum relative entropy in the first and the third quadrants is zero; Second, the minimal quantum relative entropy is symmetric with the axes y = x and y = -x even with one qubit hidden layer. Then, we theoretically prove these two features from the geometric viewpoint and the symmetry analysis. Our studies show that the traditional physical tools can be used to help us to understand some interesting results from quantum Boltzmann machine learning.