将经典感知机升级为量子版本，实现指数级加速的模式分类。
Implementing An Artificial Quantum Perceptron
- 构建量子感知机电路并模拟训练过程。
- 量子版本在模式分类中展现指数级性能优势。
- 适合对量子机器学习感兴趣的科研人员参考。
感知机是神经网络的基本单元，其灵活性与可扩展性使其广泛应用于智能系统构建。研究表明单个神经元即可实现智能决策。本文对比了两种不同机制的感知机，开发其中一种的量子版本。通过构建量子电路、生成数据集并进行仿真训练，实验表明该量子模型在模式分类任务中具有指数级增长优势，并测试了多种量子比特配置。此外，针对第二种模型，提出了脉冲依赖型量子感知机的设计与仿真方法。研究结果证明，单个量子感知机可作为有效模式分类器。代码已开源。
原文摘要 · Abstract (English)
A Perceptron is a fundamental building block of a neural network. The flexibility and scalability of perceptron make it ubiquitous in building intelligent systems. Studies have shown the efficacy of a single neuron in making intelligent decisions. Here, we examined and compared two perceptrons with distinct mechanisms, and developed a quantum version of one of those perceptrons. As a part of this modeling, we implemented the quantum circuit for an artificial perception, generated a dataset, and simulated the training. Through these experiments, we show that there is an exponential growth advantage and test different qubit versions. Our findings show that this quantum model of an individual perceptron can be used as a pattern classifier. For the second type of model, we provide an understanding to design and simulate a spike-dependent quantum perceptron. Our code is available at https://github.com/ashutosh1919/quantum-perceptron
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