Quantum Mechanics and Machine Learning

This lecture introduces the intricate nature of quantum mechanics, focusing on many-body quantum systems and the challenge of simulating them. The instructor explains the use of machine learning to study quantum problems, particularly finding the ground state of a quantum system. The lecture delves into the limitations of traditional methods due to the exponential growth of coefficients, leading to the introduction of variational methods. By parameterizing the wave function and using stochastic approaches, the lecture demonstrates how to efficiently estimate quantum properties without brute-force calculations. The connection between quantum mechanics and machine learning is explored, highlighting the importance of sampling from probability distributions to optimize parameterized wave functions.