A deep learning and hybrid quantum-classical approach to matrix quantum mechanics

Abstract

Matrix quantum mechanics plays various important roles in theoretical physics, such as a holographic description of quantum black holes. Understanding quantum black holes and the role of entanglement in a holographic setup is of paramount importance for the development of better quantum algorithms (quantum error correction codes) and for the realization of a quantum theory of gravity. Quantum computing and deep learning offer us potentially useful approaches to study the dynamics of matrix quantum mechanics. In this talk I will introduce a hybrid quantum-classical algorithm in a truncated Hilbert space suitable for finding the ground state of matrix models on NISQ-era devices. Moreover, I will discuss a deep learning approach to study the wave function of matrix quantum mechanics, even in a supersymmetric case. Results for the ground state energy will be compared to traditional Lattice Monte Carlo simulations of the Euclidean path integral as a benchmark.

Enrico Rinaldi

Research Scientist

My research interests include artificial intelligence and quantum computing applied to particle physics and quantum many-body systems.