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

Preregistered simulator and IBM hardware pilot for Lambda-IC gradient-scale diagnostics in qMERA trainability.

Companion notebook for A Technical Introduction to Quantum Neural Networks. Four small PennyLane experiments on encoding, depth and trainability, classical baselines, and finite-shot cost.

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.

Systematic comparison of four VQC data-encoding strategies (Angle, Dense Angle, IQP, Data Re-uploading) for NLP sentiment analysis, with multi-seed validation on IMDb/SST-2 and IBM Quantum hardware verification.

Simulations and analysis showing that gradient loss in noisy U(1)-equivariant quantum neural networks is governed by readout-visible sector coherence. Density-matrix simulations, regression analysis, and reproducibility code for a study of noise-induced gradient degradation in equivariant brickwork QNNs.

A reproducible toolkit for auditing symmetry-organised complexity in equivariant quantum neural network ansatz, reporting sector occupation, cross-sector coherence, sectoral fluctuation, and generator-sum compliance against U(1), SU(2), and permutation symmetry before training.

A representation-theoretic trajectory diagnostic for quantum neural networks. The symmetry-organised complexity index measures how a QNN distributes expressive capacity across the multiplicity structure of a target symmetry, rather than how much of Hilbert space it visits.

Curated GitHub Pages site tracking Quantum ML, Quantum NLP, Quantum Vision, and Hybrid Quantum-Classical AI - papers, architectures, LaTeX equations, circuit diagrams, and hardware milestones (2009–2026).

Gradient-based analysis of barren plateaus in variational quantum classifiers with VQC, hybrid VQC, MLP baselines, and ansatz diagnostics

Numerical code and source data for Barren Plateaus Beyond Observable Concentration

Weak inter-block coupling window in modular quantum circuits: entanglement/trainability tradeoff, depth law g*(L) ≈ 1.2/√L with a confirmed pre-registered prediction. Reproducible numpy POCs.