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Quantum Data Layer for Physical AI

Quantum Data
for AI Training.

Browse on Hugging Face
VQA · Barren Plateaus
SIRIUS-14k
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.
 
Molecular Chemistry
QM7b Quantum Relabeled
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.
 
Quantum Chemistry · Delta-LearningNew
SQMolecular95k
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.
 
Drug Discovery · Blood-Brain BarrierNew
BBBP Quantum Relabeled
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.
 
Quantitative FinanceNew
Quantum Finance Risk Benchmark
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.
 
Quantum Chemistry · Delta-LearningNew
SQMolecular
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.
 
ReLab Engine · Early Access

Quantum data relabelling for your AI stack.

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Quantum Benchmarks
for Physical AI.

Open, quantum-native benchmarks and evaluation suites that measure how models learn from quantum data across chemistry, biology, materials, and finance.

We partner with research groups and labs to evaluate and benchmark quantum datasets. If your team works in quantum chemistry, molecular ML, or many-body physics, reach out.

Become an Eval Partner
What We Build
Open quantum benchmarks and datasets with exact quantum labels, giving your models a clean and honest target to learn from.
How You Contribute
Bring your domain and your hardest problems. Partners help define the benchmarks and shape the datasets that matter to real research.
Built Together
We co-develop evaluation suites and datasets with each partner, turning frontier questions into reproducible, open science.
Growing Together
Every partnership strengthens the data layer that quantum AI needs, moving the whole field forward as we grow with it.