datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
QuantumChem_Testbank_3000
Hold-out test set for evaluating quantaMind.
exams-basic-and-quantum-cryptography-and-security-latex
Open Problem Exams: Cryptography and Security (LaTeX)
A curated dataset of open-ended exam problems (with solutions) in cryptography and computer security, formatted in LaTeX. The dataset is sourced from university courses at three institutions.
Dataset Overview
Institution
Files
Topics
Questions
Caltech & TU Delft
8
38
145
EPFL
6
19
86
ETH Zurich
1
14
37
MIT
3
33
79
Total
18
104
347
Difficulty Distribution
Institution… See the full description on the dataset page: https://huggingface.co/datasets/natnitaract/exams-basic-and-quantum-cryptography-and-security-latex.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-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.Qiskit-QuantumKatas
Qiskit QuantumKatas
A benchmark dataset for evaluating Large Language Models on quantum computing code generation tasks using Qiskit.
Dataset Description
This dataset contains 350 quantum computing tasks translated from Microsoft's QuantumKatas (originally in Q#) to Qiskit (Python). It is designed for evaluating LLMs on their ability to generate correct quantum computing code.
Supported Tasks
Code Generation: Given a natural language description and function… See the full description on the dataset page: https://huggingface.co/datasets/Qiskit/Qiskit-QuantumKatas.quantum-circuits-8k
Quantum Circuits 8K Dataset
A synthetic dataset of 8,129 quantum circuit examples for training language models to generate OpenQASM 2.0 code from natural language descriptions.
Quick Stats
Total Samples: 8,129 (description → QASM pairs)
Unique Circuits: 739 base circuits
Categories: 92 distinct quantum circuit types
Qubit Range: 1-9 qubits
Format: OpenQASM 2.0
Augmentation: 11x per circuit (original + 10 paraphrases)
Quality: 100% QASM syntax valid, 0% duplicates… See the full description on the dataset page: https://huggingface.co/datasets/merileijona/quantum-circuits-8k.quantum-circuits-21k
Quantum Circuits Dataset — v2 (21K)
A synthetic dataset of validated natural language → OpenQASM 2.0 circuit pairs for training quantum circuit generation models. To our knowledge the largest publicly available dataset of validated NL→QASM pairs specifically designed for generative model training.
Used to train the QuantumGPT-124M model series.
Quick Start
from datasets import load_dataset
# v2 training set (21K samples, recommended)
ds =… See the full description on the dataset page: https://huggingface.co/datasets/merileijona/quantum-circuits-21k.chess_puzzles_1k_in_pgn_san
Lichess Puzzle Database …
Lichess Puzzle Database ▸ Mate‑in‑1/2/3 Subset
A 1k slice of the official Lichess puzzle corpus. 10k soon!
Note: The board is updated to the position that arises after the opponent blunders.
Source
Original dataset: https://huggingface.co/datasets/Lichess/chess-puzzles
What’s Inside
Category
Count
Mate‑in‑1
333
Mate‑in‑2
333
Mate‑in‑3
333
Total
999
Format Improvements
Field
Old
New… See the full description on the dataset page: https://huggingface.co/datasets/quantum24/chess_puzzles_1k_in_pgn_san.chess_puzzles_10k_in_pgn_san
Lichess Puzzle Database …
Lichess Puzzle Database ▸ Mate‑in‑1/2/3 Subset
A 10k slice of the official Lichess puzzle corpus.
Note: The board is updated to the position that arises after the opponent blunders.
Source
Original dataset: https://huggingface.co/datasets/Lichess/chess-puzzles
What’s Inside
Category
Count
Mate‑in‑1
3333
Mate‑in‑2
3333
Mate‑in‑3
3333
Total
9999
Format Improvements
Field
Old
New… See the full description on the dataset page: https://huggingface.co/datasets/quantum24/chess_puzzles_10k_in_pgn_san.quantum-ood-benchmarkQuantumMechanics
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-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.quantum-api-drift
Quantum API Drift
Quantum API Drift is an evaluation benchmark for measuring whether
LLM-generated quantum code targets the requested Qiskit SDK version. It
accompanies the paper
Benchmarking API Drift in LLM-Generated Quantum Code Across Successive SDK Versions.
The benchmark evaluates version fidelity, cross-version compatibility, failure
modes, and documentation-guided repair across Qiskit 0.43, 1.3, and 2.0.
Dataset Configurations
benchmark
The… See the full description on the dataset page: https://huggingface.co/datasets/arasyi/quantum-api-drift.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.quantum-sensing-platform-settlement-1989quantum-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.anonymous_dataset
KnowVis: A Dual-View Benchmark for Diagnosing World-Knowledge Grounding in Text-to-Image Models
📖 Overview
Text-to-image (T2I) models have made substantial progress in visual realism, aesthetic quality, and instruction following. However, real-world prompts often go beyond explicit visual descriptions and require implicit facts, structured knowledge, and domain-specific commonsense. Existing evaluations mainly focus on explicit prompt-to-image semantic alignment or… See the full description on the dataset page: https://huggingface.co/datasets/QuantumWhisper42/anonymous_dataset.quantumchem-reasoningReasoning dataset for post-training Raven and QuantaMind chemisty LLMs with GRPO and RLVR.
alpaca_hindi_small
Alpaca Hindi Small
This is a synthesized dataset created by translation of alpaca dataset from English to Hindi language.
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/BNES-BRNKC/PQC-ZK-Quantum-Convergence-Layer.quantum-circuit-routing
This dataset is a remastered version prepared using Adaption's Adaptive Data platform.
quantum_circuit_routing
This dataset contains pairs of abstract OpenQASM 2.0 quantum circuits and their hardware-compliant compiled versions. Each sample specifies a target qubit topology (e.g., Star, Ring, Linear, Grid, HeavyHex) and coupling map constraints. The compiled outputs include necessary SWAP gates inserted to satisfy physical connectivity requirements while minimizing circuit… See the full description on the dataset page: https://huggingface.co/datasets/jay2219/quantum-circuit-routing.quantum-physics-corpus
quantum-physics-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: Cosine… See the full description on the dataset page: https://huggingface.co/datasets/konsman/quantum-physics-corpus.QuantumChem-200k-new
QuantumChem-200K: A Large Molecular Corpus for Chemistry Screening and Discovery. Paper under review.
code at: https://github.com/AnonymousUser-3/QuantumChem-200K.
license: GLP-3.0
cisco_cli_commandsquantum-llm-instruct-subject-knowledge
kalkiAI3000/quantum-llm-instruct-subject-knowledge
This dataset augments BoltzmannEntropy/QuantumLLMInstruct with a new field subject_knowledge,
automatically generated from each example’s main_domain, sub_domain, and problem using GPT-5.
Contents
train.json (rows: 5150): Preserves original fields and adds:
subject_knowledge (string): 3–6 concise lines summarizing definitions, governing equations,
assumptions/scales, and a typical solution strategy relevant to the… See the full description on the dataset page: https://huggingface.co/datasets/kalkiai3000/quantum-llm-instruct-subject-knowledge.chess_update_fen_1kPQC-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.Malay-quantum-language-compilationquantum-computing-2048-fineweb-sampleTest dataset for testing a data sampling and filtering script.
Configuration for filtering:
dataset: "HuggingFaceFW/fineweb-edu"
output_file: "quantum_computing_entries.jsonl"
state_file: "quantum_computing_dataset_state.json"
# Processing parameters
keywords:
- "quantum computing"
- "qubit"
- "quantum entanglement"
- "quantum supremacy"
- "quantum algorithm"
- "quantum error correction"
max_entries: 1024
min_tokens: 1024
max_tokens: 2048
min_int_score: 4
# Shuffling… See the full description on the dataset page: https://huggingface.co/datasets/Lambent/quantum-computing-2048-fineweb-sample.