Matrix Model simulations using Quantum Computing, Deep Learning, and Lattice Monte Carlo

Matrix quantum mechanics plays various important roles in theoreticalphysics, such as a holographic description of quantum black holes.Understanding quantum black holes and the role of entanglement in a holographicsetup is of paramount importance for the development of better quantumalgorithms (quantum error correction codes) and for the realization of aquantum theory of gravity. Quantum computing and deep learning offer uspotentially useful approaches to study the dynamics of matrix quantummechanics. In this paper we perform a systematic survey for quantum computingand deep learning approaches to matrix quantum mechanics, comparing them toLattice Monte Carlo simulations. In particular, we test the performance of eachmethod by calculating the low-energy spectrum