README.md

---
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