Quantifying Information Flow in Parametrized Quantum Circuits.

Variational quantum algorithms has become a highly influential paradigm for the design of near term quantum algorithms. At the heart of these algorithms lie the parametrized quantum circuit, a parametrized computation containing parameters that can be optimized in order to make the computation solve a given problem. However, this optimization can be both highly nontrivial and resource demanding, especially when there is a large number of tuneable parameters involved. In the work presented in this talk, an optimization strategy is proposed which aims to mitigate this problem by only working with a subset of the parameters in each step of the optimization. Inspired by the way that information flows through a quantum system during a computation, the method tries to help the optimizer focus only on the parameters that most heavily influences the relevant readout from the computation. To facilitate the choice of parameters, a metric for reasoning about the flow of information in a parametrized quantum circuit is proposed, and the method is benchmarked on tasks with and without local structure to highlight the strengths and weaknesses of this metric. Overall, the general method shows good promise in several tasks, while the metric-assisted version seems best suited for problems with local structure.