datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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-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-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.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-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-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.agentic-lightweight-envs-runtime-20260528
Lightweight Agentic RL Runtime Environments
This dataset contains prebuilt runtime environments for lightweight agentic reinforcement learning. It is intended to be used with the accompanying environment server/runtime code. The LLM synthesis pipeline used to create these environments is not required for serving this dataset.
Files
runtime_catalog.json.gz: prebuilt runtime task catalog consumed by the env server.
task_drafts.json.gz: source task drafts and… See the full description on the dataset page: https://huggingface.co/datasets/quantumfr/agentic-lightweight-envs-runtime-20260528.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-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.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-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.ai-auto-train-datasets-cuda-5d-quantum-mindmap-simulations-generator-zkevms-immutablexai-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.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.quantumchem-reasoningReasoning dataset for post-training Raven and QuantaMind chemisty LLMs with GRPO and RLVR.
QuantumAIquantum-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.post-quantum-crypto-en
Post-Quantum Cryptography - Dataset EN
Comprehensive English dataset on post-quantum cryptography (PQC) migration, covering NIST algorithms (FIPS 203-206), migration phases, protocol impacts, quantum threats, and 70 detailed Q&A pairs.
Dataset Contents
Category
Entries
Description
PQC Algorithms
15
ML-KEM (Kyber), ML-DSA (Dilithium), SLH-DSA (SPHINCS+), FN-DSA (FALCON), HQC, RSA, ECDSA, Ed25519, X25519, AES-256
Migration Phases
12
Cryptographic inventory… See the full description on the dataset page: https://huggingface.co/datasets/AYI-NEDJIMI/post-quantum-crypto-en.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-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.QuantumMechanics
Quantum Mechanics Reasoning Dataset
🧠 High-quality physics reasoning chains for training thinking LLMs
Dataset Overview
This dataset provides systematic reasoning chains for quantum mechanics concepts, designed specifically for training thinking LLMs like GPT-OSS-20B. Each entry contains step-by-step logical progressions with mathematical expressions and physical interpretations.
🎯 Current Status: Comprehensive Release (v1.5.0) 🆕 CHAPTER 7 ADDED
1,350 high-quality… See the full description on the dataset page: https://huggingface.co/datasets/themanaspandey/QuantumMechanics.topological-quantum-computing
Neura Parse — Topological Quantum Computing
A deep vertical dataset on topological quantum computing: the physics and computational theory of anyons and topologically ordered phases, non-abelian braiding and fusion, Majorana zero modes and the Kitaev chain, Fibonacci (universal) vs Ising (Clifford-only) anyons, topological (Majorana) qubits and measurement-only braiding protocols, the toric code as a Z2 topological phase (not merely a QEC code), fractional quantum Hall… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/topological-quantum-computing.post-quantum-crypto-fr
Cryptographie Post-Quantique - Dataset FR
Dataset francophone complet sur la migration vers la cryptographie post-quantique (PQC), couvrant les algorithmes NIST (FIPS 203-206), les phases de migration, les impacts sur les protocoles, les menaces quantiques, et 70 questions-reponses detaillees.
Contenu du dataset
Categorie
Nombre d'entrees
Description
Algorithmes PQC
15
ML-KEM (Kyber), ML-DSA (Dilithium), SLH-DSA (SPHINCS+), FN-DSA (FALCON), HQC, RSA, ECDSA… See the full description on the dataset page: https://huggingface.co/datasets/AYI-NEDJIMI/post-quantum-crypto-fr.apticraft-quantum-llm
AptiCraft — Custom AI/LLM Module (Quantum) · README
A practical guide to build, ship, and protect your Quantum Custom AI + LLM using a versioned Hugging Face dataset.
1) What this repo contains
quantum.json: user‑friendly Q&A + deep sections (algorithms, hardware, reliability, math) with routing hints.
Images: external URLs only. The app can include or suppress images per request.
Versioning: tag releases (e.g., v1, v2) and pin clients to a tag for stability.… See the full description on the dataset page: https://huggingface.co/datasets/Afsana721/apticraft-quantum-llm.Synthetic-Quantum-Reasoning-GPTOSS120b
Synthetic Quantum Reasoning Dataset (GPT-OSS 120B)
A synthetic dataset of 4,881 quantum physics reasoning chains generated using GPT-OSS 120B.
Dataset Description
Each sample contains:
instruction: A quantum physics problem/question
output: A detailed reasoning chain with <think> and <answer> tags
Format
{
"instruction": "Consider a two-qubit system...",
"output": "<think>\nStep 1: ...\n</think>\n\n<answer>\n...\n</answer>"
}
Statistics… See the full description on the dataset page: https://huggingface.co/datasets/Kylan12/Synthetic-Quantum-Reasoning-GPTOSS120b.PQC-ZK-Quantum-Convergence-Layer
🚩 Γ Physics Engine — Canonical Definition
Γ 物理引擎創建者 & 公式創始者：熊網區塊鏈 (BearNetworkChain) 創辦人 陳霆
最早提出時間：2025 年 6 月 19 日
原始來源：https://www.facebook.com/share/p/19cadcMTGo/
Chen, Ting. (2026). BearNetworkchain Execution Specification. Zenodo
📌 0. 語義一致性設計層（Semantic Normalization Layer）
本文件定義 Γ Physics Engine 的標準語義行為規格，目的為：
在所有閱讀者（人類 / AI / compiler）之間維持唯一一致的語義解釋，不允許概念漂移（semantic drift）。
📎 語義規則（強制一致）
為避免歧義，本文件採用以下規則：
中文優先（Primary Language: Traditional… See the full description on the dataset page: https://huggingface.co/datasets/BNES-BRNKC/PQC-ZK-Quantum-Convergence-Layer.bosonic-photonic-quantum-computing
Neura Parse — Bosonic, Continuous-Variable & Photonic Quantum Computing
A focused vertical on the continuous-variable and photonic route to quantum computing: bosonic error-correcting codes (cat, GKP, binomial), Gaussian and measurement-based photonic architectures, and fusion-based/dual-rail approaches — a self-contained paradigm with its own error-correction physics. Covers CV and bosonic simulation methods with Strawberry Fields / Bosonic Qiskit context, deepening the… See the full description on the dataset page: https://huggingface.co/datasets/Neura-parse/bosonic-photonic-quantum-computing.Adya-Riemann-Quantum-Corpus
Adya-Riemann Corpus: Quantum-Number Theory Bridge
Dataset Summary
This dataset introduces a novel approach to training Mathematical Reasoning models by bridging Number Theory (Prime Distributions) and Quantum Chaos (Spectral Statistics). It contains 15,000 synthetic samples generated using the Adya-Riemann Hamiltonian Framework ($H=xp + \Lambda/x^3$).
Instead of rote calculation, this corpus forces AI models to perform Isomorphic Reasoning—mapping the properties of… See the full description on the dataset page: https://huggingface.co/datasets/Adyarezapahlevi/Adya-Riemann-Quantum-Corpus.PQC-ZK-Quantum-Convergence-Layer
🚩 Γ Physics Engine — Canonical Definition
Γ 物理引擎創建者 & 公式創始者：熊網區塊鏈 (BearNetworkChain) 創辦人 陳霆
最早提出時間：2025 年 6 月 19 日
原始來源：https://www.facebook.com/share/p/19cadcMTGo/
Chen, Ting. (2026). BearNetworkchain Execution Specification. Zenodo
📌 0. 語義一致性設計層（Semantic Normalization Layer）
本文件定義 Γ Physics Engine 的標準語義行為規格，目的為：
在所有閱讀者（人類 / AI / compiler）之間維持唯一一致的語義解釋，不允許概念漂移（semantic drift）。
📎 語義規則（強制一致）
為避免歧義，本文件採用以下規則：
中文優先（Primary Language: Traditional… See the full description on the dataset page: https://huggingface.co/datasets/BearNetworkChain/PQC-ZK-Quantum-Convergence-Layer.