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.Quantum
Dataset Summary
ILSVRC 2012, commonly known as 'ImageNet' is an image dataset organized according to the WordNet hierarchy. Each meaningful concept in WordNet, possibly described by multiple words or word phrases, is called a "synonym set" or "synset". There are more than 100,000 synsets in WordNet, majority of them are nouns (80,000+). ImageNet aims to provide on average 1000 images to illustrate each synset. Images of each concept are quality-controlled and human-annotated.
💡… See the full description on the dataset page: https://huggingface.co/datasets/Miku26727/Quantum.VanGogh_vs_TreeOilPainting_QuantumTorque_EnergyField_Analysis_Phase1_2025
Dataset Policy
VanGogh Vs. Tree Oil Painting: Quantum Torque Energy Field Analysis 2025
Structure Type
Free-form and Semi-structured Narrative
Core Principles
Each file is an independent analytical entity with its own identity.
Each file is the result of Autonomous AI–Human Co-analysis.
The structure is intentionally open, flexible, and adaptive, reflecting the natural reasoning process of the researcher, rather than forcing rigid… See the full description on the dataset page: https://huggingface.co/datasets/HaruthaiAi/VanGogh_vs_TreeOilPainting_QuantumTorque_EnergyField_Analysis_Phase1_2025.quantem-data
quantem-data
This repo is currently used primarily by
quantem.widget
(tutorial notebooks and quantem.widget.datasets). Other QuantEM packages
may use it later. Keys, the checker, and these instructions can grow as
we take more Community pull requests. Use the current required keys for
new PRs.
Public electron-microscopy data. MIT license. Downloads need no token.
This page is the upload and download protocol.
Download
from quantem.widget.datasets import… See the full description on the dataset page: https://huggingface.co/datasets/bobleesj/quantem-data.Assetsimagenet-sdxl-quantized
ImageNet SDXL Quantized
This repository provides the ImageNet-1K dataset pre-encoded with the Stable Diffusion XL VAE encoder and quantized to uint8, allowing for faster training of latent diffusion models by eliminating the need for on-the-fly encoding.
Key Features
Reduces quantization error by 2dB PSNR compared to a linear encoding scheme
Provided in both 256 and 512 resolutions
Compatible with NumPy, JAX, and PyTorch
Usage
Loading the dataset… See the full description on the dataset page: https://huggingface.co/datasets/jon-kyl/imagenet-sdxl-quantized.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.VanGogh_vs_TreeOilPainting_QuantumTorque_EnergyField_Analysis_2025⚠️ Legacy Notice (Phase 1)
This dataset represents the early Center Index / Phase 1 design of the Tree Oil Painting × Van Gogh forensic framework.
It is kept online for historical transparency, methodology reference, and interface testing.
For all current physics-locked baselines, biomechanical signatures, and production-ready files, please refer to:
➡️ VanGogh_vs_TreeOilPainting_QuantumTorque_EnergyField_Analysis_2025
🌿 Interactive Image Space (Phase 1)ForensicImageGallery_Phase1… See the full description on the dataset page: https://huggingface.co/datasets/HaruthaiAi/VanGogh_vs_TreeOilPainting_QuantumTorque_EnergyField_Analysis_2025.erebus-fixtures
Erebus Rescue Maze Fixtures
Image-classification dataset of RoboCup Junior Rescue Maze "fixtures" — the victim
letters and hazardous-material placards a rescue robot must detect — captured from the
robot's camera inside the Erebus simulation (Webots).
The images were hand-collected from the robot's camera in Erebus. This Rescue Maze
category runs only in the simulator and has no physical counterpart, so these are the real
images of the task — not a synthetic stand-in for… See the full description on the dataset page: https://huggingface.co/datasets/quantumly/erebus-fixtures.Quantity-Reasoning-VQA-23KThis dataset is a part of the large TDIUC dataset. And I dont own the copyright of the work. All copyright of this dataset belongs to the original author of the work.
Uploaded here so that community member can easily access and evaluate models.
Original Paper link: https://arxiv.org/abs/1804.02088
axiom-quant-constellation
🌌 AXIOM QUANT & The Vaked Constellation
Composite Agentic Intelligence, BitNet b1.58 Ternary Quantization, Universal Threshold Kernels, and 23-Artwork Generative Substrates
Authors: Peter Lodri & The Vaked Constellation Autonomous LoopLive Ecosystem: art.vaked.dev · vision-gallery · music.vaked.dev · quant-love · proposal.vaked.dev · pocoo.vaked.dev · axiomquant.org
📌 Abstract
We present AXIOM QUANT and The Vaked Constellation, an open-source… See the full description on the dataset page: https://huggingface.co/datasets/PeetPedro/axiom-quant-constellation.safety-quant-phase0
Safety-Aware Configuration-Conditioned LoRA — Phase 0 baseline
Baseline table, frozen evaluation sets, and per-prompt judge verdicts for
meta-llama/Llama-3.2-1B-Instruct under five quantization configurations.
id
scheme
native?
c0
W16A16 bf16 (reference)
yes
c1
W8A8 (SmoothQuant + GPTQ)
yes
c2
W4A16 g128 (GPTQ)
yes
c3
W4A16 g128 (AWQ)
yes
c4
NF4 (bitsandbytes)
yes
c1sim
W8A16 g128 (GPTQ)
simulated
c2sim
W4A16 g128 (GPTQ)
simulated — the simulation control… See the full description on the dataset page: https://huggingface.co/datasets/Jeesup/safety-quant-phase0.quantum-manifold-dataVDR_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.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.QuantumEmbeddings
Overview
This repository provides a collection of embedding datasets for evaluating quantum-classical support vector machines (QSVMs) using embeddings from pre-trained classical models. Each dataset follows the naming convention:
<model_name>_<embedding_dim>.csv
Where:
model_name: the architecture used to generate the embeddings (e.g., vit_b_16, efficientnet, vit_l_14@336px)
embedding_dim: the dimensionality of the embedding vectors
The last column in each CSV represents the class… See the full description on the dataset page: https://huggingface.co/datasets/sebasmos/QuantumEmbeddings.disaggregated-quantization-blog-assetsquantum-leaderboard-assetsQuantitative_Mapping_of_Computational_Boundaries
Quantitative Mapping of Computational Boundaries
A Statistical Field Theory Approach to Phase Transitions in NP-Hard Problems
Author: Zixi Li (Oz Lee)
Affiliation: Noesis Lab (Independent Research Group)
Contact: lizx93@mail2.sysu.edu.cn
Overview
Classical computability theory tells us that computational boundaries exist (halting problem, P vs NP), but it doesn't answer: where exactly are these boundaries?
This paper presents the first quantitative mapping of… See the full description on the dataset page: https://huggingface.co/datasets/OzTianlu/Quantitative_Mapping_of_Computational_Boundaries.quantum-noise-transfer
Quantum Noise Transfer: Cross-Device Few-Shot Adaptation Dataset
Paper: Few-Shot Cross-Device Transfer for Quantum Noise Modeling on Real Hardware
Authors: Sahil Al Farib, Sheikh Redwanul Islam, Azizur Rahman Anik
Dataset Description
A real-hardware quantum noise dataset collected from two IBM Quantum devices for studying cross-device transfer learning in quantum error mitigation. Each sample pairs a noisy output distribution (measured on real hardware) with the… See the full description on the dataset page: https://huggingface.co/datasets/sahilfarib/quantum-noise-transfer.tessera-quantization-research-evidence
Tessera Quantization Research Evidence
This dataset is the primary-source measurement evidence from an ongoing research
program studying calibrated low-bit quantization (ternary, int4, vector-quantized
codebooks) for LLM inference on heterogeneous AMD hardware (RDNA3 iGPU, XDNA1/2
NPU, Zen 4/5 CPU). The work is done in a fork of llama.cpp (project name
"Tessera") that adds calibrated per-tensor ternary/payload4/VQ quantization,
NPU offload, and RDNA3-native GPU kernels.
This is… See the full description on the dataset page: https://huggingface.co/datasets/Tribunus-dev/tessera-quantization-research-evidence.lerobot_metaworld_mt50_plus_QuantilesThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"robot_type": "metaworld",
"total_episodes": 2500,
"total_frames": 204806,
"total_tasks": 49,
"chunks_size": 1000,
"fps": 80,
"splits": {
"train": "0:2500"
},
"data_path": "data/chunk-{chunk_index:03d}/file-{file_index:03d}.parquet",
"video_path": "videos/{video_key}/chunk-{chunk_index:03d}/file-{file_index:03d}.mp4"… See the full description on the dataset page: https://huggingface.co/datasets/SongMugeon/lerobot_metaworld_mt50_plus_Quantiles.qcv-dataset
QCV-Dataset
132 Quantum Circuits · 5 Core Modalities · 792 Experiment Results · Bilingual Annotations
The first multimodal quantum circuit dataset for training and evaluating AI systems on quantum circuit understanding, code generation, and verification.
Dataset Summary
QCV-Dataset contains 132 quantum circuits across 13 categories, each with 5 core modalities: circuit diagram image, Amazon Braket SDK code, Qiskit code, simulation results (state vectors), and… See the full description on the dataset page: https://huggingface.co/datasets/QuantBlockchain/qcv-dataset.cats_vs_dogs
Dataset Card for Cats Vs. Dogs
Dataset Summary
A large set of images of cats and dogs. There are 1738 corrupted images that are dropped. This dataset is part of a now-closed Kaggle competition and represents a subset of the so-called Asirra dataset.
From the competition page:
The Asirra data set
Web services are often protected with a challenge that's supposed to be easy for people to solve, but difficult for computers. Such a challenge is often called a CAPTCHA… See the full description on the dataset page: https://huggingface.co/datasets/quantumminiproject/cats_vs_dogs.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.quantarena-artifacts
QuantArena Artifact Bundle
Reproducibility artifacts for the paper QuantArena: Beat the Market or Be the
Market? A Live-Market Evaluation of Investment Paradigms (NeurIPS 2026
Evaluations & Datasets Track submission).
Summary
QuantArena is a controlled live-market evaluation protocol that holds the LLM
backend, market data stream, analyst workflow, capital, and execution harness
fixed across runs and varies only the investment doctrine (the policy
module). This bundle… See the full description on the dataset page: https://huggingface.co/datasets/NIPS26Repo/quantarena-artifacts.gtx-flickr30kall-captions-blip2-quantquantum_datasetComputation-Quantum-Physics