datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
VDR_Quantum
VDR_Quantum – Overview
VDR_Quantum is a curated multimodal dataset focused on quantum technical documents. It combines text and image data extracted from real scientific PDFs to support tasks such as RAG DSE, question answering, document search, and vision-language model training.
Dataset Composition
This dataset was created using our open-source tool VDR_pdf-to-parquet.Quantum-related PDFs were collected from public online sources. Each document was processed… See the full description on the dataset page: https://huggingface.co/datasets/racineai/VDR_Quantum.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-assistant
Quantum Assistant: Multimodal Dataset for Quantum Computing with Qiskit
The first public multimodal dataset for quantum computing code generation and understanding
Overview
Quantum Assistant Dataset is a high-quality multimodal dataset designed for specializing Vision-Language Models (VLMs) in quantum computing tasks using Qiskit. This dataset addresses the critical gap in existing quantum computing AI assistants, which operate exclusively on text and… See the full description on the dataset page: https://huggingface.co/datasets/samuellimabraz/quantum-assistant.quantum-tts-tokenized
Swahili (swa_spk3) SNAC-Tokenized Dataset for Orpheus-TTS Fine-Tuning
Dataset Summary
A single-speaker Kiswahili subset, resampled and tokenized for fine-tuning Orpheus-TTS. It is derived from rlabz/swa_lug_tts by:
Filtering the train and validation splits down to speaker swa_spk3 only.
Resampling all audio from its original 22,050 Hz to 24,000 Hz, the sample rate required by SNAC (snac_24khz), the neural audio codec Orpheus is trained on.
Encoding each clip with… See the full description on the dataset page: https://huggingface.co/datasets/rlabz/quantum-tts-tokenized.dataset-CoT-Quantum-Mechanics-1224quantum-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.Algerian-STT-Super-Dataset-V2VDR_Quantum_Circuit_Papers
VDR_Quantum_Circuit_Papers – Overview
VDR_Quantum_Circuit_Papers is a curated dataset focused on quantum circuits and quantum gates, extracted exclusively from scientific research papers. This dataset emphasizes documents that contain circuit diagrams, matrix-based explanations, and detailed discussions of quantum operations.
Dataset Composition
This dataset was created using our open-source tool VDR_pdf-to-parquet.
Scientific PDFs were sourced from public online… See the full description on the dataset page: https://huggingface.co/datasets/racineai/VDR_Quantum_Circuit_Papers.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.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}
}
Algerian-STT-Cleaned-V5QuantumLLMInstruct
QuantumLLMInstruct: A 500k LLM Instruction-Tuning Dataset with Problem-Solution Pairs for Quantum Computing
Dataset Overview
QuantumLLMInstruct (QLMMI) is a groundbreaking dataset designed to fine-tune and evaluate Large Language Models (LLMs) in the domain of quantum computing. This dataset spans 90 primary quantum computing domains and contains over 500,000 rigorously curated instruction-following problem-solution pairs.
The dataset focuses on enhancing reasoning… See the full description on the dataset page: https://huggingface.co/datasets/BoltzmannEntropy/QuantumLLMInstruct.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-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.VDR_Quantum_Circuit_Synthetic
VDR_Quantum_Circuit_Synthetic – Overview
VDR_Quantum_Circuit_Synthetic is a curated multimodal dataset focused on synthetic quantum circuits. It combines generated circuit images with expert-level technical queries to support tasks such as RAG DSE, question answering, document search, and vision-language model training.
Dataset Composition
This dataset was created using our open-source tool VDR_pdf-to-parquet, adapted to handle synthetic data.
Quantum circuit images and… See the full description on the dataset page: https://huggingface.co/datasets/racineai/VDR_Quantum_Circuit_Synthetic.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-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.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.Algerian-STT-Cleaned-V3quantum-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.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-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.cortex_quantumadvanced-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.YouToks-MIT-8.05-Quantum-Physics-II-Fall-2013quantum-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.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.graph-data-quantum