Nuclear Physics in the Era of Quantum Computing and Quantum Machine Learning

In this paper, the application of quantum simulations and quantum machine learning is explored to solve problems in low-energy nuclear physics. The use of quantum computing to address nuclear physics problems is still in its infancy, and particularly, the application of quantum machine learning (QML) in the realm of low-energy nuclear physics is almost nonexistent. Three specific examples are presented where the utilization of quantum computing and QML provides, or can potentially provide in the future, a computational advantage: i) determining the phase/shape in schematic nuclear models, ii) calculating the ground state energy of a nuclear shell model-type Hamiltonian, and iii) identifying particles or determining trajectories in nuclear physics experiments. The use of QML in the realm of nuclear physics at low energy is almost nonexistent. Three examples of the use of quantum computing and quantum machine in nuclear physics are presented: the determination of the phase/shape in nuclear models, the calculation of the ground state energy, and the identification of particles in nuclear physics experiments. image