Simulation of a Variational Quantum Perceptron using Grover's Algorithm
This work addresses the challenge of enhancing quantum machine learning for classification tasks, though it appears incremental as it builds on existing quantum components.
The paper tackled the problem of improving quantum machine learning by combining a variational quantum circuit with Grover's algorithm into a new quantum perceptron model (QVPG). The results showed that both quantum models outperformed a classical perceptron, with QVPG achieving higher accuracy than the quantum variational perceptron alone.
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.