ClarusC64/quantum-control-pulse-instability-v0.1

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