Hybrid Quantum-Classical ML

Hybrid quantum-classical machine learning studies how quantum processors can work alongside classical models, often to provide useful feature transformations or compact computational modules. Common approaches include variational quantum circuits as layers or prediction heads in neural networks, quantum kernels, quantum reservoirs, and annealing-based optimization, applied to tasks from image and time-series analysis to physics-informed modeling. The potential value is in representing complex relationships or reducing model size while using classical systems for data handling and computation. Results span small benchmarks and some real-device tests, but whether quantum components offer gains beyond matched classical controls remains an open question, alongside hardware noise, training stability, and execution cost.