Quantum Neural Architecture Search with Quantum Circuits Metric and Bayesian Optimization
This work addresses the problem of efficiently designing quantum neural networks for researchers and practitioners in quantum computing, representing an incremental advancement in quantum machine learning methods.
The paper tackled the challenge of automatic quantum neural architecture search by designing a quantum circuits metric for Bayesian optimization, resulting in significant performance improvements on three quantum machine learning problems, including training a quantum generative adversarial network, solving MaxCut, and simulating quantum Fourier transform.
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 propose a new quantum gates distance that characterizes the gates' action over every quantum state and provide a theoretical perspective on its geometrical 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.