Unsupervised machine learning quantum dynamics

As we enter the age of Artificial Intelligence (AI) and Noisy Intermediate-Scale Quantum Computing (NISQ), there has been an increased interest in applying machine learning models to quantum physics. Most applications of AI in quantum mechanics use supervised learning models such as Recurrent Neural Networks (RNN) to predict various quantum properties. However, these models are essentially performing curve fitting and have not learned the underlying dynamics found in a quantum system. In this talk, we describe the application of generative models using neural ODEs to quantum dynamics, which we show, can learn the underlying quantum dynamics and can extrapolate well beyond the training regime when performing reconstructions. Furthermore, random samples from the model satisfy the Heisenberg uncertainty principle. We apply our model to closed and open quantum system dynamics, showing that the model can distinguish between the dynamics of pure and mixed states. We demonstrate, for each hamiltonian it has trained on, the model learns an interpretable representation of the Hilbert space.