| --- |
| language: |
| - en |
| license: mit |
| pretty_name: Quantum Control Pulse Instability |
| task_categories: |
| - tabular-classification |
| tags: |
| - clarusc64 |
| - stability-reasoning |
| - quantum-computing |
| - control-pulses |
| - calibration |
| - nisq |
| - tabular |
| size_categories: |
| - n<1K |
| --- |
| |
| # quantum-control-pulse-instability-v0.1 |
|
|
| ## What this dataset does |
|
|
| This dataset evaluates whether models can detect instability in quantum control pulse regimes. |
|
|
| Each row represents a simplified control scenario where quantum gates are implemented through microwave or optical pulse sequences. |
|
|
| The task is to determine whether the pulse regime remains stable or becomes unstable due to drift, noise, or synchronization failures. |
|
|
| ## Core stability idea |
|
|
| Quantum control relies on precise pulse timing, amplitude stability, and calibration. |
|
|
| Instability emerges when control drift and noise accumulate faster than calibration and feedback mechanisms can compensate. |
|
|
| Signals that interact include: |
|
|
| - qubit count |
| - pulse amplitude stability |
| - pulse timing jitter |
| - calibration drift |
| - cross-talk |
| - thermal noise |
| - control feedback latency |
| - pulse sequence length |
| - measurement error |
|
|
| No single variable determines collapse. Instability emerges from interactions between noise, drift, and control feedback delay. |
|
|
| ## Prediction target |
|
|
| label = 1 → control pulse instability |
| label = 0 → stable control regime |
|
|
| ## Row structure |
|
|
| Each row contains proxies describing control system stability: |
|
|
| - qubit count |
| - pulse amplitude proxy |
| - pulse timing jitter proxy |
| - calibration drift proxy |
| - cross-talk proxy |
| - thermal noise proxy |
| - control feedback latency proxy |
| - pulse sequence length |
| - measurement error proxy |
|
|
| ## Evaluation |
|
|
| Predictions must follow: |
|
|
| scenario_id,prediction |
| |
| Example: |
| |
| QP101,0 |
| QP102,1 |
| |
| Run evaluation: |
| |
| python scorer.py --predictions predictions.csv --truth data/test.csv --output metrics.json |
| |
| Metrics produced: |
| |
| accuracy |
| precision |
| recall |
| f1 |
| confusion matrix |
| |
| ## Structural Note |
| |
| This dataset reflects latent quantum stability geometry expressed through observable control system proxies. |
| |
| The dataset generator and underlying stability rules are not included. |
| |
| This dataset is not a quantum hardware simulator. It is a compact stability-reasoning benchmark. |
| |
| ## License |
| |
| MIT |
