Quantum Model

Quantum models aim to leverage quantum phenomena like superposition and entanglement for enhanced machine learning capabilities, primarily focusing on improving accuracy, efficiency, and robustness compared to classical methods. Current research explores various architectures, including quantum neural networks (e.g., convolutional and recurrent types), quantum generative models (e.g., GANs and diffusion models), and quantum kernel methods, often integrated with classical components in hybrid approaches. These efforts are driven by the potential to solve complex problems in diverse fields like medical image analysis, time series forecasting, and scientific modeling, although challenges remain in training stability, resource requirements, and establishing clear quantum advantages over classical counterparts.