将随机神经网络转化为量子电路，实现量子生成人工智能。
Stochastic Neural Networks for Quantum Devices
- 基于经典感知机设计随机量子神经元，构建可优化的量子神经网络。
- 采用改进的Kiefer-Wolfowitz算法与模拟退火训练权重，支持多种网络结构。
- 可作为格罗弗算法的查询预言机，适用于量子生成模型研究者。
本文提出一种在门控量子计算中表达和优化随机神经网络的框架。受经典感知机启发，引入随机人工神经元并组合成量子神经网络。通过结合Kiefer-Wolfowitz算法与模拟退火方法进行权重训练。展示了多种拓扑结构，包括浅层全连接网络、霍普菲尔德网络、受限玻尔兹曼机、自编码器及卷积神经网络。此外，还演示了将优化后的神经网络作为格罗弗算法的预言机，实现量子生成式人工智能模型。
原文摘要 · Abstract (English)
This work presents a formulation to express and optimize stochastic neural networks as quantum circuits in gate-based quantum computing. Motivated by a classical perceptron, stochastic artificial neurons are introduced and combined into a quantum neural network. The Kiefer-Wolfowitz algorithm in combination with simulated annealing is used for training the network weights. Several topologies and models are presented, including shallow fully connected networks, Hopfield Networks, Restricted Boltzmann Machines, Autoencoders and convolutional neural networks. We also demonstrate the combination of our optimized neural networks as an oracle for the Grover algorithm to realize a quantum generative AI model.
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