Simulating a perceptron on a quantum computer
This work addresses the need for quantum equivalents of basic neural network units in quantum machine learning, though it is incremental as it builds on existing quantum algorithms.
The paper tackles the problem of simulating a classical perceptron's step-activation function on a quantum computer using the quantum phase estimation algorithm, achieving resource requirements in O(n) for input size n and enabling efficient applications in quantum neural networks.
Perceptrons are the basic computational unit of artificial neural networks, as they model the activation mechanism of an output neuron due to incoming signals from its neighbours. As linear classifiers, they play an important role in the foundations of machine learning. In the context of the emerging field of quantum machine learning, several attempts have been made to develop a corresponding unit using quantum information theory. Based on the quantum phase estimation algorithm, this paper introduces a quantum perceptron model imitating the step-activation function of a classical perceptron. This scheme requires resources in $\mathcal{O}(n)$ (where $n$ is the size of the input) and promises efficient applications for more complex structures such as trainable quantum neural networks.