Quantum Data
for AI Training.

13,800 labeled optimization trajectories across 4 VQA circuit architectures. The first public labeled dataset for barren plateau research, with gradient variance profiles, convergence diagnostics, and 24-field multi-label trainability annotations.

Quantum-relabeled subset of QM7b (7,211-molecule benchmark): 400 seven-heavy-atom molecules (300 train / 100 test) with a precomputed 7-qubit Heisenberg quantum-kernel matrix, quantum-native labels, and 1-RDM observables. Drop-in for scikit-learn precomputed-kernel pipelines, no quantum hardware required.

The 95k scale-up of SQMolecular: 94,376 geometries of exact FCI correlation energies, each paired in-file with its MP2 baseline. A harder cross-scaffold transfer benchmark that tests whether the quantum representation carries to unseen chemistry, not just unseen conformers.

Quantum-relabeled MoleculeNet BBBP benchmark for CNS drug penetration. 85 compounds encoded as a 25-qubit graph-Hamiltonian circuit, shipped as a precomputed pairwise quantum-fidelity kernel for drop-in SVM classification.

1,000 correlated-asset market regimes encoded as Ising-Hamiltonian quantum states with systemic portfolio-risk labels. Quantum features hold 0.69 test error at 16 assets where the classical kernel degrades to chance — a widening sample-efficiency gap.

10,038 exact FCI correlation energies across 717 organic molecules, 14 thermal geometries each, paired in-file with matched MP2 baselines. A noise-free delta-learning target — every bit of model error is the model's, not the label's. Produced with the ReLab engine.