Quantum Classifier

Quantum classifiers leverage quantum computing principles to improve the accuracy and robustness of machine learning classification tasks. Current research focuses on developing and benchmarking various quantum classifier architectures, including variational quantum circuits and hybrid classical-quantum models, often applied to image recognition and other pattern classification problems. A key area of investigation is enhancing the adversarial robustness of these classifiers, exploring both theoretical guarantees and practical defense mechanisms against malicious data manipulation. These efforts aim to establish the practical advantages of quantum classifiers over classical counterparts and to identify optimal design strategies for near-term quantum hardware.