Quantum-Inspired ML

Quantum-inspired machine learning uses ideas from quantum mechanics to build learning models, often running entirely on classical computers. Approaches include density-matrix and complex-valued representations, tensor networks, quantum-walk and variational-circuit-inspired layers, Kolmogorov-Arnold networks with fast weights, and QUBO optimization. These methods are tested in settings such as image and signal classification, graph learning, forecasting, anomaly detection and scientific inference, with attention to accuracy as well as model size, runtime and communication costs. Results show task-specific benefits, but no general advantage: fair comparisons with strong classical baselines, simulation overhead, scalability and generalization remain central challenges.