QML addresses this through two quantum phenomena:
Training an ML model is essentially an optimization problem. Quantum algorithms like QAOA (Quantum Approximate Optimization Algorithm) can find the "global minimum" of a loss function faster than classical gradients. cloud based quantum machine learning services
Azure integrates quantum computing with its familiar classical cloud tools. It features a "Resource Estimator" that helps ML engineers understand how many qubits they would actually need to solve a specific problem. QML addresses this through two quantum phenomena: Training