AI + Quantum
Quantum Kernel SVM
Support vector machine using quantum feature maps. Maps data into quantum Hilbert space — capturing patterns classical kernels miss on structured high-dimensional datasets.
→ Outperforms classical SVM on structured data
AI + Quantum
Variational Quantum Classifier
Parametric quantum circuit trained as a classifier. End-to-end training loop — classical optimiser tunes quantum gates. Demonstrated advantage for non-separable classification.
→ Quantum advantage for complex classification
AI + Quantum
Quantum-Seeded Neural Training
IBM hardware quantum entropy as weight initialisation seed for neural networks. True randomness eliminates symmetry-breaking failures, improves convergence reproducibility.
→ Better convergence, audit-grade seed provenance
AI + Quantum
QAOA + ML Feature Selection
Quantum-optimised feature selection for large ML datasets. QAOA finds the optimal feature subset faster than classical greedy methods on high-dimensional problems.
→ Cuts ML training time, improves accuracy
AI + Quantum
Quantum Generative Model
Quantum circuit Born machine — generates synthetic data from a learned quantum probability distribution. Applications in finance, drug discovery, and anomaly detection.
→ Quantum-native data generation, no GAN collapse
AI + Quantum
Quantum Transfer Learning
Classical feature extractor feeds into a quantum variational circuit for fine-tuned domain-specific classification. Hybrid quantum-classical transfer learning.
→ Quantum fine-tuning with minimal labelled data