README.md

---
license: apache-2.0
language:
- en
pretty_name: Quantum API Drift
size_categories:
- n<1K
source_datasets:
- extended
task_categories:
- text-generation
tags:
- code
- code-generation
- benchmark
- quantum-computing
- qiskit
- api-drift
configs:
- config_name: benchmark
  default: true
  data_files:
  - split: test
    path: data/benchmark.jsonl
- config_name: version_anchored
  data_files:
  - split: test
    path: data/version_anchored.jsonl
---

# 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](https://huggingface.co/papers/2607.04072).

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 default configuration contains 50 evaluation tasks. Each row has:

- `id`: the upstream Qiskit HumanEval task identifier
- `description`: the natural-language coding task
- `entry_point`: the function the generated code must define
- `test_call`: executable assertions used by the benchmark harness

Load it with:

```python
from datasets import load_dataset

dataset = load_dataset("arasyi/quantum-api-drift")
tasks = dataset["test"]
```

### version_anchored

This configuration contains 150 prompts: one prompt for each combination of 50
tasks and three requested SDK versions. Each row has:

- `id`: the source task identifier
- `version`: `v0`, `v1`, or `v2`
- `entry_point`: the required function name
- `test_call`: executable assertions
- `prompt`: the complete SDK-version-anchored generation prompt

The version labels map to Qiskit releases as follows:

| Label | Qiskit version |
|---|---:|
| `v0` | 0.43 |
| `v1` | 1.3 |
| `v2` | 2.0 |

Load it with:

```python
from datasets import load_dataset

dataset = load_dataset(
    "arasyi/quantum-api-drift",
    "version_anchored",
)
prompts = dataset["test"]
```

## Auxiliary Files

The `migration_notes/` directory contains the migration guidance used by the
documentation-guided repair experiments:

- `v0_to_v1.txt`: Qiskit 0.43 to 1.3
- `v1_to_v2.txt`: Qiskit 1.3 to 2.0

These files are research artifacts and are not loaded as dataset rows.

## Source and Modifications

The task descriptions and identifiers are derived from the
[Qiskit HumanEval dataset](https://huggingface.co/datasets/Qiskit/qiskit_humaneval)
and its [source repository](https://github.com/qiskit-community/qiskit-human-eval),
which are distributed under the Apache License 2.0.

Quantum API Drift modifies the upstream material by selecting 50 tasks,
adapting executable test calls for cross-version evaluation, and constructing
SDK-version-specific prompts. The modification and attribution notice is also
provided in `NOTICE`.

## Intended Use

This dataset is intended for evaluating version-aware quantum code generation,
cross-version execution compatibility, API-drift failure modes, and
documentation-guided repair. All rows are evaluation data and are published in
the `test` split; they should not be presented as a training split.

## Limitations

- The benchmark measures API-level and execution-level validity, not full
  semantic circuit correctness.
- Results depend on the execution environment and pinned Qiskit versions.
- Repair results depend on the supplied migration notes.
- The source benchmark and this derivative are public, so evaluations should
  discuss possible benchmark contamination.
- Model generations, execution traces, and aggregate paper results are not
  included in this dataset repository.

## License

The dataset is distributed under the Apache License 2.0. See `LICENSE` and
`NOTICE` for the complete terms and attribution.

## Citation

```bibtex
@misc{rasyidi2026benchmarkingapidrift,
  title={Benchmarking API Drift in LLM-Generated Quantum Code Across Successive SDK Versions},
  author={Rasyidi, Mohammad Arif and Faiz, Syahirul},
  year={2026},
  eprint={2607.04072},
  archivePrefix={arXiv},
  primaryClass={cs.SE},
  doi={10.48550/arXiv.2607.04072},
  url={https://arxiv.org/abs/2607.04072}
}
```

When reusing the underlying tasks, also cite the
[Qiskit HumanEval project](https://github.com/qiskit-community/qiskit-human-eval).