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.

Date
Sep 22, 2021 4:00 PM — 5:00 PM
Event
Invited seminar
Location
Computational Quantum Science Laboratory, EPFL, Lausanne
Enrico Rinaldi
Enrico Rinaldi
Research Scientist

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

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