datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
quantum-representations
Epsilon-Transformers Belief Analysis Dataset
This dataset contains trained neural network models and their corresponding belief state regression analysis from the Epsilon-Transformers project. The models were trained on four different stochastic processes and analyzed for their ability to learn and represent belief states.
See https://github.com/adamimos/epsilon-transformers/tree/quantum-public for codebase which generated this data.
Dataset Structure… See the full description on the dataset page: https://huggingface.co/datasets/SimplexAI/quantum-representations.qmof_quantum
Dataset Details
Dataset Description
QMOF is a database of electronic properties of MOFs, assembled by Rosen et al.
Jablonka et al. added gas adsorption properties.
Curated by:
License: CC-BY-4.0
Dataset Sources
No links provided
Citation
BibTeX:
@article{Rosen_2021,
doi = {10.1016/j.matt.2021.02.015},
url = {https://doi.org/10.1016%2Fj.matt.2021.02.015},
year = 2021,
month = {may},
publisher = {Elsevier {BV}},
volume = {4},
number =… See the full description on the dataset page: https://huggingface.co/datasets/jablonkagroup/qmof_quantum.quantum-physics-0.6-corpus
quantum-physics-0.6-corpus
Dataset Description
This is a domain-specific corpus created using ontology-guided filtering from FineWeb-Edu.
Dataset Creation
Source: HuggingFaceFW/fineweb-edu
Filtering Method: Semantic similarity to subdomain centroids (embedding-based)
Pipeline: Ontology-Guided Domain Corpus Builder
Dataset Structure
Each chunk contains:
text: The text content (256-512 tokens)
subdomain_id: Assigned subdomain
similarity_score:… See the full description on the dataset page: https://huggingface.co/datasets/konsman/quantum-physics-0.6-corpus.quantum-computing
Neura Parse — Quantum Computing
A multi-format quantum computing dataset spanning theory and hardware — from qubits, gates, and algorithms to QPUs, error correction, quantum software (Qiskit/Cirq/PennyLane), and quantum machine learning. Records come as instruction/response pairs, open and multiple-choice Q&A, runnable code tasks, encyclopedic concepts, and pretraining-style text, so the dataset supports SFT, evaluation, and continued pretraining under one schema.
Part of… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-computing.quantum-physics-0.6
quantum-physics-0.6
Dataset Description
This is a domain-specific corpus created using ontology-guided filtering from FineWeb-Edu.
Dataset Creation
Source: HuggingFaceFW/fineweb-edu
Filtering Method: Semantic similarity to subdomain centroids (embedding-based)
Pipeline: Ontology-Guided Domain Corpus Builder
Dataset Structure
Each chunk contains:
text: The text content (256-512 tokens)
subdomain_id: Assigned subdomain… See the full description on the dataset page: https://huggingface.co/datasets/konsman/quantum-physics-0.6.EveNet-TT2L-QuantumCorrelation
📄 Citation
If you use this dataset, please cite:
@article{zhang2026evenet,
title={EveNet: A Foundation Model for Particle Collision Data Analysis},
author={Zhang, Yulei and others},
journal={arXiv preprint arXiv:2601.17126},
year={2026}
}
quantum-machine-learning-theory
Neura Parse — Quantum Machine Learning Theory: Trainability, Generalization & Learning From Quantum Data
A research-depth, proof-oriented vertical on the learning theory of quantum models and quantum data. Covers why parameterized quantum circuits train or don't (barren plateaus), what they can represent, when they generalize or provably beat classical models, and — for quantum data — how to predict properties of unknown states/channels with few measurements (classical… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-machine-learning-theory.quantum-information-and-complexity-theory
Neura Parse — Quantum Information & Complexity Theory: Channels, Entropies, Classes & the Structure of Advantage
A proof-based theoretical-foundations vertical uniting quantum information theory (channels, entropies, entanglement measures, distinguishability, capacities, Shannon theory) with quantum complexity theory and the structure of quantum advantage (classes, Hamiltonian complexity, sampling-based advantage and its verification, pseudorandomness, dequantization).… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-information-and-complexity-theory.quantum-simulation-chemistry-materials
Neura Parse — Quantum Simulation of Chemistry & Materials: Encodings, VQE/QPE & Dynamics
An application-deep, code-backed vertical on simulating quantum matter: electronic-structure problems, fermion-to-qubit encodings, Hamiltonian factorizations, ground/excited-state and real-time-dynamics algorithms, and analog simulation, with end-to-end resource estimates and honest classical-competitor accounting. Built with Qiskit Nature, OpenFermion, PennyLane-QChem, and PySCF — far… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-simulation-chemistry-materials.quantum-machine-learninga continuous data scrape of arxiv and google scholar papers of quantum machine learning papers particularly regarding climate.
quantum-worldline-research
Quantum Worldline Research Data
Structured research data from the Quantum Worldline project - an AI-assisted research program investigating holographic forces in MERA tensor networks, worldline path integrals on AdS spacetime, and quantum simulation of lattice gauge theories.
Dataset Description
This dataset contains the complete structured output of the Quantum Worldline multi-agent research system, which automates the research cycle: discover - hypothesize - gate - test… See the full description on the dataset page: https://huggingface.co/datasets/Jonboy648/quantum-worldline-research.quantum-finance-risk-benchmark
Quantum vs Classical Kernels on Portfolio-Risk Structure — A Synthetic Benchmark
A synthetic benchmark for learning systemic portfolio-risk structure from a quantum
representation. As the portfolio grows from 8 to 16 assets the classical kernel degrades toward
chance (0.99) while the quantum representation keeps learning (0.69): the result is a persistent
sample-efficiency gap that widens with portfolio size.
Each example is a correlated-asset risk regime encoded as a quantum… See the full description on the dataset page: https://huggingface.co/datasets/SiriusQuantum/quantum-finance-risk-benchmark.quantum-error-mitigation-and-benchmarking
Neura Parse — Quantum Error Mitigation, Characterization & Benchmarking
A pre-fault-tolerance, code-backed vertical on getting trustworthy answers from noisy hardware and rigorously measuring device quality: error-mitigation techniques, characterization/tomography protocols, and benchmarking suites. Runnable Mitiq, pyGSTi, and Qiskit Experiments pipelines with honest sampling-overhead and bias/variance accounting — the practitioner and research toolkit the general dataset… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-error-mitigation-and-benchmarking.quantum-optimization
Neura Parse — Quantum Optimization, Annealing & Finance: QAOA, Adiabatic Methods & the Advantage Question
A research-plus-practitioner vertical on quantum approaches to combinatorial and continuous optimization and their most-piloted enterprise use cases. Covers QAOA theory and variants, adiabatic/annealing methods and D-Wave, QUBO/Ising encodings, amplitude-estimation Monte Carlo for finance, and the rigorous question of whether and where quantum beats classical (including… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-optimization.quantum-machine-learning-models
Neura Parse — Quantum Machine Learning Models: Encodings, Kernels, QNNs & Generative/Deep Architectures
A hands-on, code-first vertical on quantum models that learn from data. Spans data encodings/feature maps, variational classifiers, quantum kernels/QSVMs, and quantum neural networks through modern generative and deep architectures (quantum GANs, circuit Born machines, quantum Boltzmann machines, QCNNs, quantum autoencoders, quantum RL, and quantum… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-machine-learning-models.quantum-compilation-and-programming
Neura Parse — Quantum Compilation & Programming
A code-heavy vertical on the quantum software/compilation stack: turning abstract quantum circuits and unitaries into device-executable programs. Covers unitary decomposition and circuit synthesis (Euler/ZYZ, KAK/Cartan, Solovay-Kitaev, Ross-Selinger gridsynth, numerical synthesis with BQSKit), gate-set/basis transpilation to native gate sets, qubit layout/mapping and routing under connectivity constraints (SABRE, VF2, SWAP… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-compilation-and-programming.fault-tolerant-quantum-computing
Neura Parse — Fault-Tolerant Quantum Computing: QEC Codes, Decoders, Magic States & Resource Estimation
A deep, Stim-informed vertical on fault tolerance — QEC code families, decoders, fault-tolerant gate constructions, and the full physical-to-logical resource-estimation pipeline. Expands the general dataset's handful of error-correction topics into research-grade coverage including the 2024-2026 milestones: surface-code below threshold, qLDPC/bivariate-bicycle memories… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/fault-tolerant-quantum-computing.quantum-hardware-device-physics
Neura Parse — Quantum Hardware Device Physics: Qubit Design, Coherence, Control & Scaling
A physics- and engineering-deep vertical on how qubits are built, controlled, and scaled across superconducting, trapped-ion, neutral-atom, and spin modalities (plus emerging erasure/biased-noise qubits). Device-physics derivations, coherence-limit analyses, control-stack engineering, and 2025-2026 scaling/interconnect work, with QuTiP/scqubits simulation context — expanding the general… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-hardware-device-physics.quantum-networking-and-distributed
Neura Parse — Quantum Networking, Repeaters & Distributed Quantum Computing
A systems-frontier vertical on connecting quantum devices: entanglement distribution and distillation, quantum repeaters, quantum-internet protocol stacks, quantum memories/transduction, and modular/distributed quantum computing (nonlocal gates, circuit knitting across nodes, blind/verifiable delegated computation). Covers protocol and simulation methods used with tools such as NetSquid and SeQUeNCe… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-networking-and-distributed.ai-for-quantum
Neura Parse — AI for Quantum: ML & LLMs for Decoding, Control, Characterization & Software
The reverse quantum-AI direction — classical machine learning, RL, and LLMs/agents applied to make quantum computers work. Covers neural/transformer QEC decoders (AlphaQubit-style), RL/ML pulse and calibration control, neural-network quantum states, ML tomography and Hamiltonian/noise learning, learned circuit optimization, and LLM/agentic quantum software engineering (code generation… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/ai-for-quantum.quantum-gate-sequence-instability-v0.1
quantum-gate-sequence-instability-v0.1
What this dataset does
This dataset evaluates whether models can detect instability in quantum gate sequences.
Each row represents a simplified quantum circuit execution scenario described through observable device and circuit proxies.
The task is to determine whether the gate sequence remains executable inside a stable coherence window or becomes unstable.
Core stability idea
Quantum gate sequences become unstable when… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/quantum-gate-sequence-instability-v0.1.quantum-physics-0.5-corpus
quantum-physics-0.5-corpus
Dataset Description
This is a domain-specific corpus created using ontology-guided filtering from FineWeb-Edu.
Dataset Creation
Source: HuggingFaceFW/fineweb-edu
Filtering Method: Semantic similarity to subdomain centroids (embedding-based)
Pipeline: Ontology-Guided Domain Corpus Builder
Dataset Structure
Each chunk contains:
text: The text content (256-512 tokens)
subdomain_id: Assigned subdomain
similarity_score:… See the full description on the dataset page: https://huggingface.co/datasets/konsman/quantum-physics-0.5-corpus.quantum-perceptron-trainset
Quantum Perceptron
This project implements McCullogh-Pitts Perceptron as described in An Artificial Neuron Implemented on an Actual Quantum Processor on IBM's Qiskit quantum simulator.
The GitHub repository (https://github.com/ashutosh1919/quantum-perceptron) contains the extensive codebase to create quantum perceptron circuit based on specific weight and input data. Moreover, we created training data as shown in the paper (checkboard patterns for different numbers) and simulated… See the full description on the dataset page: https://huggingface.co/datasets/ashutosh1919/quantum-perceptron-trainset.graph-data-quantumquantum-error-correction-failure-v0.1
quantum-error-correction-failure-v0.1
What this dataset does
This dataset evaluates whether models can detect instability in quantum error correction regimes.
Each row represents a simplified quantum computing scenario where logical qubits are protected using error correction.
The task is to determine whether the correction mechanism remains stable or fails due to noise and correction latency.
Core stability idea
Quantum error correction works by detecting and… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/quantum-error-correction-failure-v0.1.quantum-sensing-and-metrology
Neura Parse — Quantum Sensing & Metrology: Fisher Information, the Heisenberg Limit & Entanglement-Enhanced Sensors
A physics- and estimation-theory-deep vertical on the second quantum revolution's sensing pillar: how quantum Fisher information and the Cramer-Rao bound set ultimate precision, how entanglement and squeezing push sensors from the standard quantum limit toward the Heisenberg limit, and how these ideas are realized in optical atomic clocks, NV-center… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-sensing-and-metrology.quantum-cryptography-and-post-quantum-security
Neura Parse — Quantum Cryptography & Post-Quantum Security
A deep vertical on cryptography that uses quantum mechanics and on classical cryptography built to resist quantum attack. It covers quantum key distribution (BB84, B92, six-state, SARG04, E91, BBM92, decoy-state, MDI-QKD, TF-QKD, CV-QKD), device-independent protocols, composable and finite-key security proofs, quantum hacking with countermeasures, classical post-processing (reconciliation, privacy amplification… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/quantum-cryptography-and-post-quantum-security.quantum-noise-transfer
Quantum Noise Transfer: Cross-Device Few-Shot Adaptation Dataset
Paper: Few-Shot Cross-Device Transfer for Quantum Noise Modeling on Real Hardware
Authors: Sahil Al Farib, Sheikh Redwanul Islam, Azizur Rahman Anik
Dataset Description
A real-hardware quantum noise dataset collected from two IBM Quantum devices for studying cross-device transfer learning in quantum error mitigation. Each sample pairs a noisy output distribution (measured on real hardware) with the… See the full description on the dataset page: https://huggingface.co/datasets/sahilfarib/quantum-noise-transfer.quantum-ground-states-320-qubits
Quantum Ground States — Exact Representations to 320 Qubits
Exact quantum ground states of disordered transverse-field Ising chains, from 20 to 320
qubits, each paired with its local quantum representation and a physical label. A benchmark for
learning properties of quantum states at a scale no state-vector simulator can reach — every
example is exact ground truth.
Benchmark
Predict the physical observable Σᵢ ⟨ZᵢZᵢ₊₁⟩ from a representation of the ground state. Two… See the full description on the dataset page: https://huggingface.co/datasets/SiriusQuantum/quantum-ground-states-320-qubits.advanced-quantum-algorithms
Neura Parse — Advanced Quantum Algorithms: Derivations, QSVT/Block-Encoding & Hamiltonian Simulation
A derivation- and resource-analyzed algorithms vertical spanning the canonical fault-tolerant canon (with full proofs, complexity, and worked traces) and the modern QSVT/block-encoding toolkit through Hamiltonian simulation, amplitude estimation, and quantum linear systems. Turns the general dataset's one-topic-per-algorithm summaries into line-by-line derivations, lower… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/advanced-quantum-algorithms.