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.

The CREX and PREX experiments at Jefferson Laboratory use parity violating electron scattering to determine neutron densities. The PREX measurement for 208Pb constrains the density dependence of the symmetry energy and the pressure of neutron rich matter. This has important implications for neutron stars. The CREX measurement for 48Ca provides a test of microscopic chiral effective field theory calculations and the importance of three neutron forces. In addition, these measurements can aid in the interpretation of atomic parity and coherent neutrino scattering experiments. We describe the CREX experiment, present the remarkably accurate results, and compare to earlier PREX results.