Datasets:

ClarusC64
/

quantum-control-pulse-instability-v0.1

scenario_id string	qubit_count int64	pulse_amplitude_proxy float64	pulse_timing_jitter_proxy float64	calibration_drift_proxy float64	cross_talk_proxy float64	thermal_noise_proxy float64	control_feedback_latency_proxy float64	pulse_sequence_length int64	measurement_error_proxy float64	label int64
QP001	12	0.72	0.14	0.12	0.18	0.16	0.2	38	0.1	0
QP002	16	0.48	0.36	0.34	0.42	0.38	0.44	96	0.3	1
QP003	10	0.76	0.12	0.1	0.16	0.14	0.18	34	0.09	0
QP004	18	0.46	0.38	0.36	0.46	0.4	0.48	104	0.34	1
QP005	14	0.7	0.16	0.14	0.2	0.18	0.22	42	0.11	0
QP006	20	0.44	0.4	0.38	0.5	0.44	0.52	112	0.38	1
QP007	9	0.78	0.1	0.09	0.15	0.13	0.16	32	0.08	0
QP008	17	0.5	0.34	0.32	0.44	0.36	0.46	98	0.32	1
QP009	13	0.71	0.15	0.13	0.19	0.17	0.21	40	0.1	0
QP010	22	0.42	0.42	0.4	0.54	0.46	0.56	120	0.42	1
QP011	11	0.75	0.13	0.11	0.17	0.15	0.19	36	0.09	0
QP012	24	0.4	0.44	0.42	0.58	0.5	0.6	128	0.46	1
QP013	14	0.7	0.16	0.14	0.2	0.18	0.22	42	0.11	0
QP014	18	0.46	0.38	0.36	0.46	0.4	0.48	104	0.34	1
QP015	9	0.78	0.1	0.09	0.15	0.13	0.16	32	0.08	0

quantum-control-pulse-instability-v0.1

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.

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:

No single variable determines collapse. Instability emerges from interactions between noise, drift, and control feedback delay.

label = 1 → control pulse instability
label = 0 → stable control regime

Each row contains proxies describing control system stability:

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

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.

MIT