datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
task_data
QuantCodeEval
A benchmark for evaluating LLM coding agents on quantitative-strategy code
reproduction from finance research papers.
Status: Anonymous artifact for the 30-task benchmark.
Release mirrors
The release is mirrored at two anonymous locations:
Hugging Face Datasets — complete anonymous release:
https://huggingface.co/datasets/quantcodeeval/task_data
anonymous.4open.science — browseable mirror:
https://anonymous.4open.science/r/QuantCodeEval-Anonymous… See the full description on the dataset page: https://huggingface.co/datasets/quantcodeeval/task_data.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-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.hemmingway-1-omlx-quantization-evidence-v2
Hemmingway-1 Quantization Evidence v2
This package records two local evidence lanes for the Hemmingway-1 oQ4e build: teacher-forced numerical fidelity against a BF16 reference, and controlled runtime telemetry on Apple Silicon. It complements the frozen blind-preference study in Hemmingway-1 oMLX Quantization Benchmark v1.
This dataset is sixstringzen/hemmingway-1-omlx-quantization-evidence-v2. The quality dataset remains unchanged because blind preference, distribution fidelity… See the full description on the dataset page: https://huggingface.co/datasets/sixstringzen/hemmingway-1-omlx-quantization-evidence-v2.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.hemmingway-1-omlx-quantization-benchmark-v1
Hemmingway-1 oMLX Quantization Benchmark
This is the public-safe benchmark package for the Hemmingway-1 oMLX
quantization study on Apple Silicon.
Altworld developed and published
Hemmingway-1. Bobby Pierce
published these quantizations and the evaluation package. The
collection
links the upstream model and all six builds.
Analysis revision 2, corrected on 2026-09-22, fixes A/B attribution and matching
across reversed packets. Read CORRECTION.md before using the
aggregate… See the full description on the dataset page: https://huggingface.co/datasets/sixstringzen/hemmingway-1-omlx-quantization-benchmark-v1.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.opensre-incident-trajectories
OpenSRE Incident-Diagnosis Trajectories
Graded, multi-step SRE incident-diagnosis trajectories. A frozen LLM reads evidence through
diagnostic tools (describe_pod / get_events / get_logs / get_metrics / query_traces / …),
states a root cause + category + fix, and is scored on substance against ground truth. Built as a
HUD v6 RL environment with a deliberate model spanning set so difficulty is legible and the
within-group reward spread is real (the GRPO learning signal).
197… See the full description on the dataset page: https://huggingface.co/datasets/quantranger/opensre-incident-trajectories.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-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.lean-quantfinance
Lean 4 Formalized Quantitative Finance & Game Theory
A domain-specific Lean 4 / Mathlib corpus centered on finance and market
mechanisms: 2,074 theorem records + 887 definitions, extracted from a
formalization pipeline and packaged for theorem-proving research (statement,
proof, tactics, premises, kernel-axiom status).
This is a mechanization of largely standard applied mathematics, not new
finance theory. Its value is breadth in under-formalized areas — market
microstructure… See the full description on the dataset page: https://huggingface.co/datasets/seancollins/lean-quantfinance.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.sre-agent-eda-bundle
SRE-Agent Data Bundle
This is a consolidated exploratory-data-analysis (EDA) bundle of four separate data bodies from an SRE (site-reliability-engineering) incident-diagnosis / remediation research program: graded agent rollouts, the scenario corpus the agents run against, GRPO training reward logs, and a harness A/B evaluation on a live cluster. It is intended for ML/SRE teammates who want to load, slice, and interrogate the raw records — not as a leaderboard or a… See the full description on the dataset page: https://huggingface.co/datasets/quantranger/sre-agent-eda-bundle.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-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-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.pot-o-quantum-88
pot-o-quantum-88
888,888 synthetic PoT-O challenges for tensor + MML path optimization in a Planck–tensor–realm lineage used to probe REALMS / realms-devkit themes: Planck-scale framing (Part I), tensor-network / entropy toy scales (Part IV §3–§4), and coarse-resolution “realm” tensor shapes (including 8×8 and 88×8-style blocks).
Program scale (8.88M lineage): each row carries nominal_lineage_M: 8.88 (documentation of the Quantum-88 / 8.88M Tribewarez program scale). This dataset… See the full description on the dataset page: https://huggingface.co/datasets/Tribewarez/pot-o-quantum-88.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.awq-quant-evals
AWQ Quant Quality Evals
Side-by-side AWQ W4A16 vs BF16 quality measurements for:
Quant
Base
Suite
LostGentoo/Qwen3.5-4B-AWQ
Qwen/Qwen3.5-4B
OpenLLM-lite
LostGentoo/Qwen3-Embedding-8B-AWQ
Qwen/Qwen3-Embedding-8B
MTEB-lite
Hardware: NVIDIA RTX 5060 Ti (sm_120, Blackwell).
Files
File
Contents
quant_quality_evals.json
Full combined report
qwen35_4b_awq_vs_bf16.json
LLM OpenLLM-lite only
qwen3_embedding_8b_awq_vs_bf16.json
Embedding… See the full description on the dataset page: https://huggingface.co/datasets/LostGentoo/awq-quant-evals.