quantum-kernels

Quantum kernel estimation with backend-matched IBM noise modeling, plus reproducible “Wigner’s friend” branch-transfer coherence-witness experiments executed on superconducting quantum hardware.

Verification harness for quantum ML. A reproducible lab for stress-testing quantum models where predictive accuracy, identifiability, curvature, and robustness under noise can diverge.

Hybrid RAG pipeline combining BM25, dense retrieval, quantum kernels, Grover amplification and QAOA reranking.

Reproducibility artifact for 'Sharp Target-Domain Certificates for Quantum-Kernel Advantage under Distribution Shift': sharp finite-batch certificates, controlled quantum-vs-classical kernel benchmark (EMBER, UNSW-NB15, ToN-IoT, TableShift), frozen specifications, prediction locks, manuscript sources. Zenodo v1.1.6.

Quantum LeJEPA: applying LeJEPA self-supervised learning to quantum feature maps, and the second-moment operator M2 that predicts quantum-kernel trainability without training. Personal research.

Entangled group-invariant quantum kernels for visual symmetry and anomaly experiments.

COMP47950: compare classical ML, simulated QML (VQC & QSVM), and IBM Quantum inference-only evaluation on Nim classification.

Hands-on quantum computing for machine learning, built from scratch in NumPy then bridged to PennyLane. 6-week course: qubits & gates, entanglement & CHSH, quantum algorithms (Deutsch–Jozsa, Grover, QFT), variational circuits & VQE, quantum ML classifiers, and quantum kernels.

Reproducibility code and summary artifacts for 'Exact Transfer-Matrix Evaluation Reveals Operating-Point Limits and Opportunities for Fidelity Quantum Kernels in Biomedical Abstract Screening' (QSVC for biomedical abstract screening).