Quantum Perceptron

Quantum perceptrons are a fundamental building block in quantum machine learning, aiming to leverage quantum mechanics for improved classification and other machine learning tasks. Current research focuses on developing and optimizing various quantum perceptron architectures, including variational quantum perceptrons often combined with algorithms like Grover's search, and exploring hybrid quantum-classical models. These efforts investigate the performance and stability of these models, often comparing them to classical counterparts on benchmark datasets like MNIST, and address challenges related to scalability and physical realizability on near-term quantum devices. The ultimate goal is to determine whether quantum perceptrons offer a practical advantage over classical approaches for specific applications.