Differentiable programming tensor networks and quantum circuits*

Computation is playing an increasingly important role in the studies of complex quantum systems. Efficient and exact gradients from automatic differentiation (AD) changes the way we program. This talk covers a brief survey of the state of the art differential programming frameworks, and their applications to condensed matter physics and quantum computing. These application range from optimizing infinite tensor network states to simulating variational quantum algorithms. Lastly, I will introduce reversible computing as the host of next generation differential programming framework, which may unleash the full power of AD for differentiable scientific computing.