metadata
license: mit
language:
- en
- zh
pretty_name: QCV-Dataset
tags:
- quantum-computing
- quantum-circuits
- code-generation
- multimodal
- image-to-text
- braket
- qiskit
- science
- physics
- machine-learning
- bilingual
task_categories:
- image-to-text
- text-generation
- visual-question-answering
task_ids:
- image-captioning
- text2text-generation
- visual-question-answering
size_categories:
- n<1K
annotations_creators:
- expert-generated
- machine-generated
language_creators:
- expert-generated
multilinguality:
- multilingual
source_datasets:
- original
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 Description
- Curated by: Dongping Liu, Aoyu Zhang, Luyao Zhang
- Language(s): English (EN), Chinese (CN) — bilingual annotations
- License: MIT
- Modality: Multimodal — Images (circuit diagrams), Text (code + descriptions), Numerical (state vectors)
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 bilingual expert annotations. Additionally, 792 experimental model invocations (3 models × 2 prompting modes × 132 circuits) provide a comprehensive benchmark for evaluating visual AI agents on quantum code generation.
Dataset Structure
Config: circuits (default)
| Feature | Type | Description |
|---|---|---|
id |
string | Unique circuit identifier (e.g., C01_deutsch_jozsa_3) |
circuit_image |
Image | Qiskit-generated circuit diagram (PNG, 150 DPI, IQP style) |
braket_code |
string | Amazon Braket SDK executable Python code |
qiskit_code |
string | Qiskit equivalent implementation |
description_en |
string | English algorithm description |
description_cn |
string | Chinese algorithm description |
category |
string | Circuit category (13 categories) |
difficulty |
string | Difficulty level: basic, intermediate, advanced |
qubits |
int32 | Number of qubits (1–10) |
gate_count |
int32 | Number of gates (or null) |
depth |
int32 | Circuit depth (1–27) |
blockchain_relevance |
string | Blockchain relevance tag (if applicable) |
state_vector_dim |
int32 | Dimension of state vector (2^qubits) |
nonzero_amplitudes |
int32 | Number of nonzero amplitudes |
state_vector_real |
sequence[float64] | Real components of simulated state vector |
state_vector_imag |
sequence[float64] | Imaginary components of simulated state vector |
target_description |
string | Target task description |
best_pass_rate |
string | Best pass rate across all models (e.g., "5/6") |
all_pass |
bool | Whether circuit passed all model-mode combinations |
all_fail |
bool | Whether circuit failed all model-mode combinations |
Config: experiments
| Feature | Type | Description |
|---|---|---|
circuit_id |
string | Reference to circuit |
model |
string | Model name (claude-opus-4.6, claude-sonnet-4.6, claude-haiku-4.5) |
mode |
string | Prompting mode (bv = base vision, tv = thinking vision / chain-of-thought) |
syntax_ok |
bool | Whether generated code compiles |
exec_ok |
bool | Whether code executes without runtime errors |
fidelity |
float64 | Unitary matrix fidelity score |
pass |
bool | Whether verification passed (fidelity >= 0.99) |
error |
string | Error message (if failed) |
Config: failures
Annotated failure cases from model evaluation with error type classification.
Config: equivalences
Circuit equivalence pairs for verification benchmarking.
Categories (13)
| ID | Category | Count | Qubits |
|---|---|---|---|
| demo | Basic Gates | 5 | 1–3 |
| inter | Intermediate | 10 | 2–4 |
| adv | Advanced Algorithms | 6 | 3–5 |
| blockchain | Blockchain Protocols | 11 | 2–8 |
| A | Gate Type Coverage | 15 | 1–3 |
| B | Qubit Scaling | 12 | 4–10 |
| C | Classical Algorithms | 15 | 2–4 |
| D | Variational/Parameterized | 10 | 2–4 |
| E | Error Correction | 8 | 3–9 |
| F | Quantum ML | 10 | 2–8 |
| G | Blockchain Extended | 8 | 3–6 |
| H | Visual Variants | 10 | 2–4 |
| I | BTC/Blockchain Security | 12 | 4–7 |
Dataset Creation
Data Collection
- Circuit diagrams generated with Qiskit
QuantumCircuit.draw("mpl", style="iqp")at 150 DPI with tight bounding boxes - Ground-truth code implemented in Amazon Braket SDK
- All circuits verified executable on Amazon Braket
LocalSimulator
Annotations
- Bilingual descriptions (EN/CN) created by domain experts
- Categories assigned based on algorithm type and complexity
- Difficulty levels determined by circuit depth and gate complexity
Experiment Results
| Model | BV Pass% | TV Pass% | Credits/Correct |
|---|---|---|---|
| Claude Opus 4.6 | 78% | 75% | 0.778 |
| Claude Sonnet 4.6 | 77% | 75% | 0.142 |
| Claude Haiku 4.5 | 43% | 46% | 0.072 |
Key Findings:
- 45 circuits passed all 6 model-mode combinations
- 18 circuits failed all 6 combinations
- Structural complexity (not qubit count) determines success
- Chain-of-thought provides no benefit for strong models (delta = -3 to -4%) but modest improvement for weakest (delta = +5%)
Usage
Load the dataset
from datasets import load_dataset
# Load main circuits dataset
circuits = load_dataset("QuantBlockchain/qcv-dataset", "circuits", split="train")
# Load experiment results
experiments = load_dataset("QuantBlockchain/qcv-dataset", "experiments", split="train")
# Access a sample
sample = circuits[0]
print(sample["id"]) # C01_deutsch_jozsa_3
print(sample["circuit_image"]) # PIL.Image object
print(sample["braket_code"]) # Python code string
print(sample["description_en"]) # English description
print(sample["description_cn"]) # Chinese description
Filter by category
algo_circuits = circuits.filter(lambda x: x["category"] == "classical_algorithms")
small_circuits = circuits.filter(lambda x: x["qubits"] <= 3)
passing_circuits = circuits.filter(lambda x: x["all_pass"] == True)
Analyze experiment results
from collections import Counter
model_pass = {}
for exp in experiments:
model = exp["model"]
if model not in model_pass:
model_pass[model] = {"total": 0, "passed": 0}
model_pass[model]["total"] += 1
if exp["pass"]:
model_pass[model]["passed"] += 1
for model, stats in model_pass.items():
rate = stats["passed"] / stats["total"] * 100
print(f"{model}: {rate:.1f}% ({stats['passed']}/{stats['total']})")
Data Governance & Croissant
This dataset follows Croissant metadata standards for machine-readable dataset descriptions. The dataset card uses structured YAML front matter for discoverability and includes:
- Data provenance: Synthetic generation via Qiskit + expert curation
- Annotation methodology: Expert-generated bilingual descriptions
- Verification protocol: Unitary matrix fidelity >= 0.99 on Braket LocalSimulator
- Known limitations: Framework-specific (Braket SDK), simulation-only, EN/CN bilingual only
- Bias considerations: 23.5% blockchain-relevant circuits may skew toward cryptographic applications
The dataset also includes a Croissant-RAI (croissant-rai.jsonld) extension documenting responsible AI considerations, data limitations, and recommended use cases.
Limitations and Biases
| Limitation | Description |
|---|---|
| Framework lock-in | Code is Amazon Braket SDK specific |
| Simulation gap | No hardware execution data; LocalSimulator results may differ from real QPUs |
| Language coverage | Bilingual EN/CN only |
| Depth range | 1-27; may not represent extremely deep circuits |
| Domain skew | 23.5% blockchain-relevant circuits over-represents cryptographic applications |
Citation
@misc{liu2026qcv,
title={QCV: Cost-Aware Evaluation of Visual AI Agents for Quantum Code Generation},
author={Liu, Dongping and Zhang, Aoyu and Zhang, Luyao},
year={2026},
url={https://github.com/QuantBlockchain/quantum-circuit-vision}
}
License
MIT — see LICENSE
Additional Documentation
- DATASHEET.md — Full dataset documentation following Gebru et al. (2021)
- CITATION.cff — Machine-readable citation metadata
- CIRCUIT_CATALOG.md — Full listing of all 132 circuits