qubitclient-scope

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• SKILL.md

SKILL.md (first lines)

## Installation

This skill depends on the `qubitclient` package. **Install it before running any analysis** 

```bash
pip install qubitclient
```


## Overview

Scope is a traditional numerical curve fitting module for quantum experiment analysis. It provides comprehensive parameter extraction tasks including peak detection, relaxation time fitting (T1/T2), oscillation analysis (Rabi, Ramsey), pulse optimization (DRAG), and 2D spectrum analysis. Unlike neural network-based approaches, Scope uses classical fitting algorithms for interpretable results.

## API Reference

### Client Initialization

```python
# Scope tasks (curve fitting & analysis)
from qubitclient import QubitScopeClient, TaskName
client = QubitScopeClient()
```

### Task Names

#### Scope Tasks (TaskName)
| TaskName | Description |
|----------|-------------|
| `S21PEAK` | Single peak detection with confidence score |
| `S21PEAKMULTI` | Multi-peak detection across full frequency range |
| `OPTPIPULSE` | Optimal π-pulse calculation |
| `RABICOS` | Rabi oscillation cosine first peak detection |
| `RAMSEY` | Ramsey fringe decay oscillation fitting |
| `S21VSFLUX` | S21 vs Flux analysis |
| `SINGLESHOT` | Single-shot readout analysis |
| `SPECTRUM` | Frequency spectrum analysis (AMPD algorithm) |
| `T1FIT` | T1 relaxation time exponential fitting |
| `T2FIT` | T2 coherence time fitting (Gaussian + exponential decay + cosine oscillation) |
| `SPINECHO` | Spin Echo T2 relaxation time fitting |
| `SPECTRUM2D` | 2D spectrum curve segmentation |
| `POWERSHIFT` | Power shift curve analysis |
| `DRAG` | DRAG anti-crossing point analysis |
| `RB` | Randomized benchmarking fidelity test |
| `DELTA` | Delta optimization experiment |
| `T12DFIT` | 2D T1 relaxation time fitting |
| `TIMINGXYZ` | XYZ Timing calibration analysis |
| `OPTREADFREQ` | Optimal readout frequency selection |

### Data Input Formats

The input data should be a dictionary or list of dictionaries with the following structure:

```python
dict_list = [{
    "image": {
        "Q0": [x_data, y_data],  # or [x_data, y_data, z_data] for 2D
        "Q1": [x_data, y_data],
    }
} ...]  # One or more data items
```

**Note:** Multiple data items are supported - pass a list of dictionaries. The output `results` list has one entry per input item, in the same order.

### Task-Specific Formats

#### S21PEAK / S21PEAKMULTI

**Input:**
```python
{
    "image": {
        "Q0": [x_array, amp_array, phi_array],  # (freq, amplitude, phase)
        "Q1": [x_array, amp_array, phi_array],
    },
}
```
- `x_array`: 1D frequency array, shape (A,)
- `amp_array`: 1D amplitude array (magnitude of complex S21), shape (A,)
- `phi_array`: 1D phase array (unwrapped, detrended), shape (A,)

**Output:**
```json
{
  "type": "s21peak",
  "results": [{
    "peaks": [[int, ...], [int, ...]],              // Peak indices per qubit
    "confs": [[float, ...], [float, ...]],          // Confidence scores per peak
    "freqs_list": [[float, ...], [float, ...]],     // Peak frequencies per qubit
    "status": "success" | "failed"
  }]
}
```

---

#### OPTPIPULSE

**Input:**
```python
{
    "image": {
        "Q0": [waveforms_array, x_array],  // (2D waveform data, 1D time axis)
        "Q1": [waveforms_array, x_array],
    }
}
```
- `waveforms_array`: 2D array of shape (n_waveforms, n_points)
- `x_array`: 1D array of time values

Supported data keys: `population`, `iq_avg`, `iq`

**Output:**
```json
{
  "type": "optpipulse",
  "results": [{
    "params": [[float, ...], [float, ...]],   // Co-peak time positions per qubit
    "confs": [[float, ...], [float, ...]],    // Confidence scores per peak
    "status": "success" | "failed"

From yaqiangsun/QubitClient, MIT.

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