Purifying Deep Boltzmann Machines for Thermal Quantum States

Understand

We develop two cutting-edge approaches to construct deep neural networks representing the purified finite-temperature states of quantum many-body systems.

• Both methods commonly aim to represent the Gibbs state by a highly expressive neural-network wave function, exemplifying the idea of purification.
• The first method is an entirely deterministic approach to generate deep Boltzmann machines representing the purified Gibbs state exactly.
• This strongly assures the remarkable flexibility of the ansatz which can fully exploit the quantum-to-classical mapping.