用于量子设备的随机神经网络
Stochastic Neural Networks for Quantum Devices
AI总结：
本文提出利用量子电路实现随机神经网络，结合优化算法构建多种模型，并用于Grover算法实现量子生成式人工智能。
摘要：
本文提出了一种表达和优化随机神经网络作为量子电路的公式，用于基于门的量子计算。受经典感知机启发，引入了随机神经元并组合成量子神经网络。使用Kiefer-Wolfowitz算法与模拟退火算法训练网络权重。展示了多种拓扑结构和模型，包括浅层全连接网络、Hopfield网络、受限玻尔兹曼机、自编码器和卷积神经网络。我们还展示了将优化后的神经网络作为Grover算法的Oracle，以实现量子生成式人工智能模型。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
