datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.quants
QuAnTS: Question Answering on Time Series
QuAnTS is a challenging dataset designed to bridge the gap in question-answering research on time series data.
The dataset features a wide variety of questions and answers concerning human movements, presented as tracked skeleton trajectories.
QuAnTS also includes human reference performance to benchmark the practical usability of models trained on this dataset.
At present, there is no official leaderboard for this dataset.
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/dasyd/quants.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.gmat-quant-corpus
GMAT Quant Corpus for Solver + Retrieval
This dataset is intended for retrieval over GMAT-style quantitative teaching content.
Files
gmat_hf_chunks.jsonl — retrieval chunks used by the app
gmat_question_seed.jsonl — question seed data
gmat_topic_index.json — topic metadata/index
Default dataset viewer
The default dataset viewer is configured to load only:
gmat_hf_chunks.jsonl
This avoids schema conflicts with the other support files in the repository.
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-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-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-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-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.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.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-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.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.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.gpqa
Dataset Card for GPQA
GPQA is a multiple-choice, Q&A dataset of very hard questions written and validated by experts in biology, physics, and chemistry. When attempting questions out of their own domain (e.g., a physicist answers a chemistry question), these experts get only 34% accuracy, despite spending >30m with full access to Google.
We request that you do not reveal examples from this dataset in plain text or images online, to reduce the risk of leakage into foundation… See the full description on the dataset page: https://huggingface.co/datasets/quantiles/gpqa.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-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.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.MMLU-Pro
MMLU-Pro Dataset
MMLU-Pro dataset is a more robust and challenging massive multi-task understanding dataset tailored to more rigorously benchmark large language models' capabilities. This dataset contains 12K complex questions across various disciplines.
|Github | 🏆Leaderboard | 📖Paper |
🚀 What's New
[2026.03.11] Added more cutting-edge frontier models to the leaderboard, including the Claude-4.6 series, Seed2.0 series, Qwen3.5 series, and Gemini-3.1-Pro… See the full description on the dataset page: https://huggingface.co/datasets/quantiles/MMLU-Pro.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.MNLP_M3_quantized_dataset
Enhanced MCQA Test Dataset for Comprehensive Model Evaluation
This dataset contains 400 carefully selected test samples from MetaMathQA, AQuA-RAT, OpenBookQA, and SciQ datasets, designed for comprehensive MCQA (Multiple Choice Question Answering) model evaluation and quantization testing across multiple domains.
Dataset Overview
Total Samples: 400
MetaMathQA Samples: 100 (mathematical problems)
AQuA-RAT Samples: 100 (algebraic word problems)
OpenBookQA Samples: 100… See the full description on the dataset page: https://huggingface.co/datasets/AlirezaAbdollahpoor/MNLP_M3_quantized_dataset.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.reddit-sre-corpus
Reddit SRE + Founder corpus (v2)
1475 unique posts scraped from 15 subreddits between 2013-06-14 and 2026-06-19. Built for product discovery on the Kubernetes Incident Autopilot hypothesis — agents that reason at inference time through incident diagnosis, with humans in an override loop.
Subreddits (15)
SRE tier: r/sre, r/devops, r/kubernetes, r/sysadmin, r/aws, r/azure, r/gcp, r/programming, r/ExperiencedDevs, r/chaosengineering
Founder tier: r/Entrepreneur… See the full description on the dataset page: https://huggingface.co/datasets/quantranger/reddit-sre-corpus.MNLP_M2_quantized_dataset
MCQA Test Dataset for Model Evaluation
This dataset contains 3254 carefully selected test samples from MetaMathQA and AQuA-RAT datasets, designed for MCQA (Multiple Choice Question Answering) model evaluation and quantization testing.
Dataset Overview
Total Samples: 3254
MetaMathQA Samples: 3000 (mathematical problems)
AQuA-RAT Samples: 254 (algebraic word problems)
Question Types: Math, Algebra
Intended Use: Model evaluation, quantization benchmarking
Source… See the full description on the dataset page: https://huggingface.co/datasets/AlirezaAbdollahpoor/MNLP_M2_quantized_dataset.cleand_moremilk_CoT_Reasoning_Quantom_Physics_And_Computing元データ: https://huggingface.co/datasets/moremilk/CoT_Reasoning_Quantom_Physics_And_Computing
使用したコード: https://github.com/LLMTeamAkiyama/0-data_prepare/tree/master/src/CoT_Reasoning_Quantom_Physics_And_Computing
データ件数: 2,862
平均トークン数: 1,110
最大トークン数: 2,334
合計トークン数: 3,175,666
ファイル形式: JSONL
ファイル分割数: 1
合計ファイルサイズ: 15.5 MB
加工内容：
メタデータ列の解析と新列生成: metadata列（辞書型）を解析し、その中のreasoningをthought列に、difficultyをdifficulty列に展開しました。解析に失敗した行は除外されました。また、元のmetadata列は削除されました。
難易度によるフィルタリング:… See the full description on the dataset page: https://huggingface.co/datasets/LLMTeamAkiyama/cleand_moremilk_CoT_Reasoning_Quantom_Physics_And_Computing.