Quantum Neural Architecture Search with Quantum Circuits Metric and Bayesian Optimization

• URL: http://arxiv.org/abs/2206.14115v1
• Date: Tue, 28 Jun 2022 16:23:24 GMT
• Title: Quantum Neural Architecture Search with Quantum Circuits Metric and Bayesian Optimization
• Authors: Trong Duong, Sang T. Truong, Minh Tam, Bao Bach, Ju-Young Ryu, June-Koo Kevin Rhee
• Abstract summary: We propose a new quantum gates distance that characterizes the gates' action over every quantum state. Our approach significantly outperforms the benchmark on three empirical quantum machine learning problems.
• Score: 2.20200533591633
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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