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README.md

@@ -1,84 +1,256 @@
 ---
-dataset_info:
-- config_name: circuits
-  features:
-  - name: id
-    dtype: string
-  - name: circuit_image
-    dtype: image
-  - name: braket_code
-    dtype: string
-  - name: qiskit_code
-    dtype: string
-  - name: category
-    dtype: string
-  - name: difficulty
-    dtype: string
-  - name: qubits
-    dtype: int32
-  - name: gate_count
-    dtype: int32
-  - name: depth
-    dtype: int32
-  - name: description_en
-    dtype: string
-  - name: description_cn
-    dtype: string
-  - name: blockchain_relevance
-    dtype: string
-  - name: state_vector_dim
-    dtype: int32
-  - name: nonzero_amplitudes
-    dtype: int32
-  - name: state_vector_real
-    list: float64
-  - name: state_vector_imag
-    list: float64
-  - name: target_description
-    dtype: string
-  - name: best_pass_rate
-    dtype: string
-  - name: all_pass
-    dtype: bool
-  - name: all_fail
-    dtype: bool
-  splits:
-  - name: train
-    num_bytes: 2360453
-    num_examples: 132
-  download_size: 2325540
-  dataset_size: 2360453
-- config_name: experiments
-  features:
-  - name: circuit_id
-    dtype: string
-  - name: model
-    dtype: string
-  - name: mode
-    dtype: string
-  - name: syntax_ok
-    dtype: bool
-  - name: exec_ok
-    dtype: bool
-  - name: fidelity
-    dtype: float64
-  - name: pass
-    dtype: bool
-  - name: error
-    dtype: string
-  splits:
-  - name: train
-    num_bytes: 54003
-    num_examples: 792
-  download_size: 8778
-  dataset_size: 54003
+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
+  - code-generation
+  - visual-reasoning
+size_categories:
+  - n<1K
+annotations_creators:
+  - expert-generated
+  - machine-generated
+language_creators:
+  - expert-generated
+multilinguality:
+  - multilingual
+source_datasets:
+  - original
 configs:
-- config_name: circuits
-  data_files:
-  - split: train
-    path: circuits/train-*
-- config_name: experiments
-  data_files:
-  - split: train
-    path: experiments/train-*
+  - config_name: circuits
+    data_files:
+      - split: train
+        path: data/circuits-*.parquet
+  - config_name: experiments
+    data_files:
+      - split: train
+        path: data/experiments-*.parquet
+  - config_name: failures
+    data_files:
+      - split: train
+        path: data/failures-*.parquet
+  - config_name: equivalences
+    data_files:
+      - split: train
+        path: data/equivalences-*.parquet
 ---
+
+# 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
+
+```python
+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
+
+```python
+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
+
+```python
+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](https://github.com/mlcommons/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
+
+```bibtex
+@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](LICENSE)
+
+## Additional Documentation
+
+- [DATASHEET.md](https://github.com/QuantBlockchain/quantum-circuit-vision/blob/main/DATASHEET.md) — Full dataset documentation following Gebru et al. (2021)
+- [CITATION.cff](https://github.com/QuantBlockchain/quantum-circuit-vision/blob/main/CITATION.cff) — Machine-readable citation metadata
+- [CIRCUIT_CATALOG.md](https://github.com/QuantBlockchain/quantum-circuit-vision/blob/main/CIRCUIT_CATALOG.md) — Full listing of all 132 circuits