Quantum Gravity in the Lab: matrix quantum mechanics meets quantum computing


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. For this reason, I will discuss a first benchmark of such techniques to simple models of matrix quantum mechanics. First, 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. Then, I will discuss a deep learning approach to study the wave function of matrix quantum mechanics, even in a supersymmetric case, using a neural network representation of quantum states. Results for the ground state energy will be compared to traditional Lattice Monte Carlo simulations of the Euclidean path integral as a benchmark.

May 18, 2022 1:00 PM — 2:30 PM
Seminar at the Particle Theory group at the University of Tokyo (Komaba campus)
Komaba Particle Theory group at the University of Tokyo, Japan
Enrico Rinaldi
Enrico Rinaldi
Research Scientist

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