Simulation of a Variational Quantum Perceptron using Grover's Algorithm
Nouhaila Innan Mohamed Bennai
The quantum perceptron, the variational circuit, and the Grover algorithm
have been proposed as promising components for quantum machine learning. This
paper presents a new quantum perceptron that combines the quantum variational
circuit and the Grover algorithm. However, this does not guarantee that this
quantum variational perceptron with Grover's algorithm (QVPG) will have any
advantage over its quantum variational (QVP) and classical counterparts. Here,
we examine the performance of QVP and QVP-G by computing their loss function
and analyzing their accuracy on the classification task, then comparing these
two quantum models to the classical perceptron (CP). The results show that our
two quantum models are more efficient than CP, and our novel suggested model
QVP-G outperforms the QVP, demonstrating that the Grover can be applied to the
classification task and even makes the model more accurate, besides the
unstructured search problems.