Duplicate from phanerozoic/qiskit-calibration-drift

Co-authored-by: Charles Norton <phanerozoic@users.noreply.huggingface.co>

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README.md

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+---
+license: cc-by-4.0
+task_categories:
+- time-series-forecasting
+- tabular-classification
+tags:
+- quantum-computing
+- IBM-Quantum
+- calibration
+- hardware-characterization
+- drift-analysis
+- qiskit
+- space-weather
+- cosmic-rays
+pretty_name: IBM Quantum Calibration Drift
+size_categories:
+- 1K<n<10K
+configs:
+- config_name: default
+  data_files:
+  - split: train
+    path: data/train-*
+dataset_info:
+  features:
+  - name: backend
+    dtype: string
+  - name: qubit
+    dtype: int64
+  - name: property
+    dtype: string
+  - name: value
+    dtype: float64
+  - name: calibrated_time
+    dtype: string
+  - name: observed_time
+    dtype: string
+  - name: location
+    dtype: string
+  - name: latitude
+    dtype: float64
+  - name: longitude
+    dtype: float64
+  - name: solar_zenith_deg
+    dtype: float64
+  - name: temperature_c
+    dtype: float64
+  - name: pressure_hpa
+    dtype: float64
+  - name: humidity_pct
+    dtype: float64
+  - name: kp_index
+    dtype: float64
+  - name: solar_flux_sfu
+    dtype: float64
+  - name: dst_nt
+    dtype: float64
+  - name: bz_gsm_nt
+    dtype: float64
+  - name: neutron_flux
+    dtype: float64
+  splits:
+  - name: train
+    num_bytes: 30865256
+    num_examples: 155368
+  download_size: 1065305
+  dataset_size: 30865256
+---
+
+# IBM Quantum Calibration Drift Dataset
+
+Continuously-updated calibration data from IBM Quantum hardware with concurrent environmental measurements. Enables correlation analysis between qubit performance and atmospheric/space weather conditions.
+
+## Overview
+
+| Property | Value |
+|----------|-------|
+| Update frequency | Every 30 minutes |
+| Backends | ibm_fez (156 qubits), ibm_torino (133 qubits), ibm_marrakesh (156 qubits) |
+| Total qubits | 445 |
+| Collection method | Automated polling via GitHub Actions |
+| Calibration source | IBM Quantum Runtime API |
+| Weather source | NWS API (NOAA) |
+| Space weather source | SWPC (NOAA) |
+
+## Schema
+
+| Field | Type | Description |
+|-------|------|-------------|
+| `backend` | string | Backend identifier |
+| `qubit` | int | Qubit index (0 to N-1), or -1 for two-qubit gate data |
+| `property` | string | Calibration property name |
+| `value` | float | Measured value |
+| `calibrated_time` | string | IBM calibration timestamp (UTC) |
+| `observed_time` | string | Collection timestamp (UTC ISO) |
+| `location` | string | Data center location identifier |
+| `latitude` | float | Data center latitude |
+| `longitude` | float | Data center longitude |
+| `solar_zenith_deg` | float | Solar zenith angle (>90° = night) |
+| `temperature_c` | float | Local temperature (°C) |
+| `pressure_hpa` | float | Barometric pressure (hPa) |
+| `humidity_pct` | float | Relative humidity (%) |
+| `kp_index` | float | Planetary K-index (0-9, geomagnetic activity) |
+| `solar_flux_sfu` | float | 10.7cm solar radio flux (SFU) |
+| `dst_nt` | float | Dst index (nT, ring current strength) |
+| `bz_gsm_nt` | float | IMF Bz component (nT, negative = geo-coupling) |
+| `neutron_flux` | float | Cosmic ray proxy (Newark, DE monitor) |
+
+### Important: Timestamp Interpretation
+
+This dataset contains two timestamp fields with very different meanings:
+
+- **`calibrated_time`** — When IBM last calibrated that specific property. These timestamps can span months because IBM does not recalibrate all properties simultaneously. Some properties (like T1/T2) may retain calibration timestamps from weeks or months ago.
+
+- **`observed_time`** — When the poller actually collected the record. This reflects the true data collection period.
+
+For time-series analysis, use `observed_time`. The `calibrated_time` span reflects IBM's stale cache, not the collection period.
+
+### Calibration Properties
+
+**Per-qubit:**
+- `T1` — Relaxation time (seconds)
+- `T2` — Dephasing time (seconds)
+- `readout_error` — Measurement error probability
+- `prob_meas0_prep1` — P(measure 0 | prepared 1)
+- `prob_meas1_prep0` — P(measure 1 | prepared 0)
+- `sx_error` — SX gate error (native single-qubit gate)
+
+**Per-edge:**
+- `cz_error_{i}_{j}` — Two-qubit CZ gate error for edge (i, j)
+
+### Environmental Fields
+
+**Solar position:**
+- `solar_zenith_deg` — Sun angle from vertical (0°=overhead, 90°=horizon, >90°=night)
+
+**Weather (local to data center):**
+- `temperature_c` — Ambient temperature
+- `pressure_hpa` — Barometric pressure (correlates with cosmic ray flux attenuation)
+- `humidity_pct` — Relative humidity
+
+**Space weather (global):**
+- `kp_index` — Geomagnetic storm indicator (0=quiet, 9=severe storm)
+- `solar_flux_sfu` — Solar activity proxy (higher = more active sun)
+- `dst_nt` — Ring current strength (< -50 nT = storm, < -100 nT = severe)
+- `bz_gsm_nt` — Interplanetary magnetic field z-component (negative = geomagnetic coupling)
+- `neutron_flux` — Cosmic ray flux from Newark, DE monitor (pressure-corrected)
+
+## Data Center Locations
+
+| Location ID | Coordinates | Backends |
+|-------------|-------------|----------|
+| yorktown_heights_ny | 41.27°N, 73.78°W | ibm_torino, ibm_fez, ibm_marrakesh |
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+ds = load_dataset("phanerozoic/qiskit-calibration-drift", split="train")
+
+# Filter by backend
+torino = ds.filter(lambda x: x["backend"] == "ibm_torino")
+
+# Correlation analysis: T1 vs pressure
+t1_data = ds.filter(lambda x: x["property"] == "T1")
+df = t1_data.to_pandas()
+correlation = df["value"].corr(df["pressure_hpa"])
+
+# Filter by space weather conditions
+storm_data = ds.filter(lambda x: x["kp_index"] >= 5)
+```
+
+## Research Applications
+
+- **Cosmic ray correlation**: Barometric pressure modulates atmospheric shielding against cosmic rays, which cause quasiparticle poisoning in superconducting qubits.
+- **Geomagnetic storm effects**: Kp index tracks magnetospheric disturbances that may correlate with qubit coherence.
+- **Solar cycle tracking**: Dataset spans the declining phase of Solar Cycle 25 (peaked October 2024).
+- **Seasonal/diurnal patterns**: Long-term collection enables detection of periodic environmental effects.
+
+**Note:** Environmental correlation studies require weeks to months of continuous `observed_time` coverage. Check the current observation window before attempting such analyses.
+
+## Collection Method
+
+Data is collected via GitHub Actions every 30 minutes:
+1. Fetch space weather (NOAA SWPC)
+2. Fetch local weather (NWS API)
+3. Query IBM Quantum calibration data
+4. Deduplicate by (backend, qubit, property, calibrated_time)
+5. Append new records to dataset
+
+Source: [github.com/CharlesCNorton/qiskit-calibration-drift](https://github.com/CharlesCNorton/qiskit-calibration-drift)
+
+## Citation
+
+```bibtex
+@dataset{qiskit-calibration-drift,
+  title={IBM Quantum Calibration Drift Dataset},
+  author={Norton, Charles C.},
+  year={2026},
+  publisher={Hugging Face},
+  url={https://huggingface.co/datasets/phanerozoic/qiskit-calibration-drift}
+}
+```
+
+## Acknowledgments
+
+We acknowledge the NMDB database (www.nmdb.eu), founded under the European Union's FP7 programme (contract no. 213007) for providing neutron monitor data.
+
+## License
+
+CC-BY-4.0

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