  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 paper we perform a systematic survey for quantum computing
and deep learning approaches to matrix quantum mechanics, comparing them to
Lattice Monte Carlo simulations. In particular, we test the performance of each
method by calculating the low-energy spectrum.
