Keywords: quantum neural network, quantum neural architecture search, Bayesian optimization
TL;DR: We automate quantum neural architecture search by designing a quantum circuit metric for Bayesian optimization.
Abstract: Quantum neural networks are promising for a wide range of applications in the Noisy Intermediate-Scale Quantum era. As such, there is an increasing demand for automatic quantum neural architecture search. We tackle this challenge by designing a quantum circuits metric for Bayesian optimization with Gaussian process. To this goal, we develop quantum gates distance that characterizes the gates' action over every quantum state and provide a theoretical perspective on its geometric properties. Our approach significantly outperforms the benchmark on three empirical quantum machine learning problems including training a quantum generative adversarial network, solving combinatorial optimization in the MaxCut problem, and simulating quantum Fourier transform. Our method can be extended to characterize behaviors of various quantum machine learning models.
Track: Original Research Track