Quantum mechanics is at the core of our understanding of the subatomic world. Moreover, it is driving a new industry based on quantum computing technology. It is hard to think that quantum mechanics is wrong. On the other hand, general relativity successfully describes gravity at the macroscopic scale. It predicts black holes and gravitational waves, and it is needed to design GPS devices. It is hard to imagine that general relativity is wrong. However, physicists have been struggling to make sense of quantum mechanics and general relativity simultaneously. This is one of the deepest problems in physics. Hawking found that black holes can emit particles and evaporate due to quantum-mechanical effects, therefore providing a promising connection between the two theories. This observation led to the so-called information paradox, and serious efforts to resolve the information paradox led to the discovery of the holographic principle, which allows us to describe quantum gravitational systems, such as evaporating black holes, in terms of more standard quantum mechanical systems. Matrix models are particularly interesting in this context. Additionally, they have been used in several application fields as material science and quantum encryption. By solving the dynamics of matrix models, we can obtain valuable insights into quantum gravity. It is difficult to solve matrix models with traditional methods, but deep learning and quantum computers can be game-changers. In this talk, I will connect different research fields and introduce the first comprehensive study of quantum technologies and deep learning methods applied to matrix models.