Automatic Differentiable Monte Carlo: Applications

Differentiable programming emerges as the new programming paradigm empowering the rapid development of deep learning as it has been shown equally powerful in computational physics. By introducing automatic differentiable Monte Carlo (ADMC), we can leverage state-of-the-art machine learning frameworks and techniques to traditional Monte Carlo approaches in statistics and physics by simply implementing relevant Monte Carlo algorithms on computation graphs. We show the power of ADMC by three specific applications from physics and statistics: 1. Locate the critical temperature for 2D Ising model; 2. Compute Fisher matrix with automatic differentiation setup; 3. End-to-end, easy-to-implement, automatic differentiable variational Monte Carlo on 2D Heisenberg model with general neural network wavefunction anstaz. We further discuss about other potential possibilities that ADMC bring us in the innovations and breakthroughs of Monte Carlo methods.