00Open datasets

Open quantum datasets, published where builders work.

One umbrella corpus and sixteen domain verticals share a single schema for training, evaluation, and continued pretraining.

huggingface.co/Neura-parse · CC-BY-4.0 · EN · Parquet · train/test

Open the Hugging Face orgGitHub org

01Corpus model

Start broad, specialize on a vertical, then evaluate on its held-out split. The shared schema keeps that route consistent across the collection.

Instruction / response

Supervised fine-tuning (SFT) of assistants and copilots

Open Q&A

Free-form evaluation and retrieval-grounded answering

Multiple choice

Deterministic scoring and regression benchmarks

Runnable code tasks

Code-generation training and execution-checked evaluation

Concepts + pretraining text

Continued pretraining and encyclopedic grounding

Start here

Umbrella dataset
DS-00Quantum Computing (umbrella)Neura-parse/quantum-computingThe general multi-format dataset spanning theory and hardware: qubits, gates, and algorithms through QPUs, error correction, quantum software, and quantum machine learning. Instruction/response pairs, open and multiple-choice Q&A, runnable code tasks, encyclopedic concepts, and pretraining-style text under one schema.quantum-computingqiskitquantum-machine-learningsynthetic

02Dataset ledger

All sets: EN · Parquet · train + test · CC-BY-4.0
G013 sets

Proof-oriented verticals on what quantum computation is and where advantage comes from.

DS-01 · DS-02 · DS-03

G022 sets

Both directions: quantum models that learn from data, and classical ML that makes quantum computers work.

DS-04 · DS-05

G035 sets

From device physics to the physical-to-logical resource pipeline, simulated in code.

DS-06 · DS-07 · DS-08 · DS-09 · DS-10

G043 sets

The compilation stack, quantum simulation of matter, and the honest advantage question.

DS-11 · DS-12 · DS-13

G053 sets

Distributed quantum systems, precision measurement, and the quantum-safe boundary.

DS-14 · DS-15 · DS-16

Open the full dataset directorySummaries, repository IDs, tags, and direct Hugging Face links

Proof-oriented verticals on what quantum computation is and where advantage comes from.

DS-01Quantum Information & Complexity TheoryNeura-parse/quantum-information-and-complexity-theoryChannels, entropies, entanglement measures, distinguishability, and capacities united with complexity classes and the structure of quantum advantage.quantum-channelsentropybqpqmaDS-02QML Theory: Trainability & GeneralizationNeura-parse/quantum-machine-learning-theoryWhy parameterized circuits train or don't (barren plateaus), what they represent, when they provably beat classical models, and classical shadows for learning from quantum data.barren-plateausgeneralizationclassical-shadowsDS-03Advanced Quantum AlgorithmsNeura-parse/advanced-quantum-algorithmsThe fault-tolerant canon with full derivations plus the modern QSVT/block-encoding toolkit through Hamiltonian simulation, amplitude estimation, and quantum linear systems.qsvtblock-encodinghamiltonian-simulationgrover

Both directions: quantum models that learn from data, and classical ML that makes quantum computers work.

From device physics to the physical-to-logical resource pipeline, simulated in code.

DS-06Quantum Hardware Device PhysicsNeura-parse/quantum-hardware-device-physicsHow qubits are built, controlled, and scaled across superconducting, trapped-ion, neutral-atom, and spin modalities, with runnable QuTiP/scqubits simulations.superconducting-qubitstrapped-ionsquantum-controlDS-07Fault-Tolerant QC: Codes, Decoders & Magic StatesNeura-parse/fault-tolerant-quantum-computingA Stim-backed vertical on QEC code families, decoders, fault-tolerant gate constructions, and full physical-to-logical resource estimation.surface-codeqldpcmagic-state-distillationstimDS-08Error Mitigation, Characterization & BenchmarkingNeura-parse/quantum-error-mitigation-and-benchmarkingTrustworthy answers from noisy hardware: ZNE, PEC, dynamical decoupling, tomography, and benchmarking with runnable Mitiq, pyGSTi, and Qiskit Experiments pipelines.zero-noise-extrapolationrandomized-benchmarkingmitiqDS-09Bosonic, CV & Photonic Quantum ComputingNeura-parse/bosonic-photonic-quantum-computingCat, GKP, and binomial codes, Gaussian and measurement-based photonic architectures, and fusion-based approaches with runnable CV simulations.gkp-codecat-qubitsphotonicmeasurement-basedDS-10Topological Quantum ComputingNeura-parse/topological-quantum-computingAnyons and topological order, non-abelian braiding and fusion, Majorana zero modes, Fibonacci versus Ising anyons, and the toric code as a Z2 topological phase.anyonsmajorana-zero-modestoric-codebraiding

The compilation stack, quantum simulation of matter, and the honest advantage question.

DS-11Quantum Compilation & ProgrammingNeura-parse/quantum-compilation-and-programmingTurning circuits and unitaries into device-executable programs: synthesis, transpilation, layout and routing (SABRE, VF2), ZX-calculus, OpenQASM 3, and QIR.transpilationcircuit-synthesisopenqasm3zx-calculusDS-12Quantum Simulation of Chemistry & MaterialsNeura-parse/quantum-simulation-chemistry-materialsElectronic structure, fermion-to-qubit encodings, Hamiltonian factorizations, VQE/QPE and real-time dynamics, built with Qiskit Nature, OpenFermion, PennyLane-QChem, and PySCF.vqeelectronic-structureopenfermionpyscfDS-13Quantum Optimization, Annealing & FinanceNeura-parse/quantum-optimizationQAOA theory and variants, adiabatic and annealing methods, QUBO/Ising encodings, amplitude-estimation Monte Carlo for finance, and the rigorous where-does-quantum-win question.qaoaqubo-isingquantum-annealingquantum-finance

Distributed quantum systems, precision measurement, and the quantum-safe boundary.

DS-14Quantum Networking, Repeaters & Distributed QCNeura-parse/quantum-networking-and-distributedEntanglement distribution and distillation, repeaters, quantum-internet protocol stacks, memories and transduction, protocol- and simulation-backed with NetSquid and SeQUeNCe.quantum-internetrepeatersdistributed-qcDS-15Quantum Sensing & MetrologyNeura-parse/quantum-sensing-and-metrologyQuantum Fisher information and the Cramér-Rao bound, squeezing from the standard quantum limit toward the Heisenberg limit, realized in atomic clocks, NV magnetometers, and interferometry.fisher-informationheisenberg-limitnv-centersDS-16Quantum Cryptography & Post-Quantum SecurityNeura-parse/quantum-cryptography-and-post-quantum-securityQKD protocol families from BB84 through MDI/TF/CV-QKD, device-independent protocols, security proofs, quantum hacking countermeasures, and the NIST PQC suite (ML-KEM, ML-DSA, SLH-DSA, HQC).qkdml-kemcrypto-agilitycertified-randomness

03Data flows

The corpus is built for three flows. Each one ends in something reviewable, because a model you cannot evaluate is a liability: every dataset ships a held-out test split, and in our own stack the experiments that consume these sets are recorded as QFlow evidence.

Dataset-to-evidence pathThe held-out split is a control boundary, not another training source.
  1. 01input

    Select the umbrella corpus or a domain vertical and retain its dataset identity and licence.

    Input · dataset revision

  2. 02process

    Read instruction, question, code, concept, and pretraining records through the shared schema.

    Artifact · common fields

  3. 03gate

    Keep published train and held-out test splits distinct before tuning or evaluation begins.

    Gate · split integrity

  4. 04process

    Use the selected records for supervised tuning, deterministic evaluation, or grounded retrieval.

    Process · declared use

  5. 05output

    Retain the dataset revision, method, test result, and limitation as reviewable model evidence.

    Output · evaluation record

Training, evaluation, and evidence notesThree supported flows and the provenance rationale
Instruction/response and code-task records tune assistants and copilots on quantum domains — from Qiskit-era programming through QEC and compilation. Train on the umbrella, specialize on a vertical.
Held-out test splits with open and multiple-choice Q&A give deterministic scoring for regression tests: measure a base model, measure it after tuning, keep the delta as evidence.
Encyclopedic concepts and pretraining-style text extend a base model's domain knowledge, and double as retrieval corpora for RAG systems that must answer quantum questions with citations.

The collection follows the same research practice as QANTIS, qmesh, and QMANN, and feeds the assistants and evaluation harnesses built with NowFlow and QFlow. It remains open under CC-BY-4.0.

04Quickstart

Every dataset loads through the standard datasets library with a train and test split. Attribution under CC-BY-4.0: credit Neura Parse Ltd and link the dataset.

Format
Parquet
Splits
train / test
Language
English
License
CC-BY-4.0
load_dataset · Neura-parse
pip install datasets

from datasets import load_dataset

# The umbrella corpus — survey depth across the field
ds = load_dataset("Neura-parse/quantum-computing")

# A deep-dive vertical — research depth on one domain
ft = load_dataset("Neura-parse/fault-tolerant-quantum-computing")

print(ds["train"][0])   # one schema across all 17 sets
print(ft["test"].num_rows)

Use the corpus

Seventeen open datasets under one schema. If you are building quantum tooling, assistants, or evaluation pipelines on top of them, we want to hear about it.

huggingface.co/Neura-parseTalk to the team