Predicting Properties of Quantum Systems with Conditional Generative Models

Understand

Machine learning has emerged recently as a powerful tool for predicting properties of quantum many-body systems.

• For many ground states of gapped Hamiltonians, generative models can learn from measurements of a single quantum state to reconstruct the state accurately enough to predict local observables.
• Alternatively, classification and regression models can predict local observables by learning from measurements on different but related states.
• In this work, we combine the benefits of both approaches and propose the use of conditional generative models to simultaneously represent a family of states, learning shared structures of different quantum states from measurements.