论文标题
量子神经结构搜索量子电路指标和贝叶斯优化
Quantum Neural Architecture Search with Quantum Circuits Metric and Bayesian Optimization
论文作者
论文摘要
在嘈杂的中间量子时代，量子神经网络对于广泛的应用有希望。因此，对自动量子神经架构搜索的需求不断增长。我们通过设计高斯工艺的贝叶斯优化的量子电路指标来应对这一挑战。为了实现这一目标，我们提出了一个新的量子门距离，该距离是在每个量子状态上的作用，并就其几何特性提供了理论观点。我们的方法极大地超过了三个经验量子机学习问题的基准，包括培训量子生成的对抗网络，在maxcut问题中求解组合优化以及模拟量子傅立叶变换。我们的方法可以扩展以表征各种量子机学习模型的行为。
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.
