Quantum Perceptron

Here, we introduce how quantum computing can lead to a revolution in perceptron models. We develop a novel approach that integrates the classical and quantum paradigms within neural computation. We present two quantum algorithms that significantly boost perceptron learning. First, it approximates sublinear time in N of the computation of a separating hyperplane, and then it improves the classical mistake bound from O(1/γ2 ) to O(1/γ ). We also introduce the Quantum Perceptron Network, which uses quantum phase for the purpose of performing complex functions like XOR with one neuron; this is simply impossible to achieve with a classical perceptron. Thus, work that exploits such principles as linear superposition and quantum gates opens up new avenues in artificial intelligence and control engineering and points toward quantum neural networks redefining potential computational limits and learning capabilities in future AI systems.