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.