Variational Quantum Classifier

Variational Quantum Classifiers (VQCs) are hybrid quantum-classical algorithms aiming to leverage quantum computing for improved classification tasks. Current research focuses on enhancing VQC performance through architectural innovations like incorporating geometric inductive biases and evolutionary algorithms to mitigate training challenges such as barren plateaus, as well as exploring hybrid approaches combining VQCs with classical methods like deep neural networks and transfer learning. These advancements demonstrate VQCs' potential for superior accuracy and efficiency in diverse applications, including medical diagnosis, financial fraud detection, and material science, although scalability and the need for efficient classical pre- and post-processing remain active research areas.