Using the Pinch-Off Models | Conductor Quantum

Installation

We recommend using pip, poetry, or uv to install the package.

$ pip install conductorquantum

Authentication

The SDK requires an API key for authentication. Sign in and create a new API key. Remember, your API key is your access secret—keep it safe with environment variables.

Using environment variables:

Python

1 from dotenv import load_dotenv
2 import os
3 from conductorquantum import ConductorQuantum
4 
5 # Load API key from .env file
6 load_dotenv()
7 TOKEN = os.getenv("CONDUCTOR_API_KEY")
8 
9 # Initialize client
10 client = ConductorQuantum(token=TOKEN)

Or provide the API key directly:

Python

1 from conductorquantum import ConductorQuantum
2 
3 # Initialize client with API key
4 client = ConductorQuantum(token="YOUR_API_KEY")

Usage Examples

Using the Pinch-Off Classifier

The Pinch-Off Classifier is a model that can classify a given current measurement as either exhibiting Pinch-Off or not.

You can download an example file to follow along with the example:

Python

1 from dotenv import load_dotenv
2 import os
3 import numpy as np
4 from conductorquantum import ConductorQuantum
5 
6 
7 # Load API key from .env file
8 load_dotenv()
9 TOKEN = os.getenv("CONDUCTOR_API_KEY")
10 
11 # Initialize client
12 client = ConductorQuantum(token=TOKEN)
13 
14 # Load Pinch-off data (current measurement)
15 data = np.load("pinch-off-classifier-v0.npy")  # shape (n, )
16 
17 # Detect Pinch-off
18 result = client.models.execute(
19     model="pinch-off-classifier-v0",
20     data=data
21 )
22 
23 # Access the classification result
24 is_pinch_off = result.output["classification"]
25 print(f"Is pinch-off: {is_pinch_off}")

Output

1 Is pinch-off: True

Using the Pinch-Off Parameter Extractor

The Pinch-Off Parameter Extractor is a model that can extract the parameters of a given current measurement.

You can download an example file to follow along with the example:

Python

1 from dotenv import load_dotenv
2 import os
3 import numpy as np
4 from conductorquantum import ConductorQuantum
5 
6 
7 # Load API key from .env file
8 load_dotenv()
9 TOKEN = os.getenv("CONDUCTOR_API_KEY")
10 
11 # Initialize client
12 client = ConductorQuantum(token=TOKEN)
13 
14 # Load Pinch-off data (current measurement)
15 data = np.load("pinch-off-parameter-extractor-v0.npy")  # shape (n, )
16 
17 # Detect Pinch-off
18 result = client.models.execute(
19     model="pinch-off-parameter-extractor-v0",
20     data=data
21 )
22 
23 # Access the peak locations (indices)
24 pinch_off_parameters = result.output
25 print(f"Pinch-off parameters: {pinch_off_parameters}")

Output

1 Pinch-off parameters: {'cut_off_index': 31, 'transition_index': 43, 'saturation_index': 54}

Plotting the Output

Python

1 import matplotlib.pyplot as plt
2 
3 plt.figure(figsize=(10, 5), dpi=300)
4 plt.plot(example_input, 'k-')
5 plt.axvline(x=result_api.output["cut_off_index"], color='b', linestyle='--', label='Cutoff Index')
6 plt.axvline(x=result_api.output["transition_index"], color='g', linestyle='--', label='Transition Index')
7 plt.axvline(x=result_api.output["saturation_index"], color='r', linestyle='--', label='Saturation Index')
8 plt.xlabel('Voltages (Indices)')
9 plt.ylabel('Current (a.u.)')
10 plt.legend()
11 plt.show()

Data Requirements

One-dimensional Current Data

Shape: (n, )
1D array of current values

Important Notes for Voltage-Current Data

The array must be of shape (n, ) where n is the number of current values in the measurement.
Analysis is outputted in terms of indices of the input array.
The models automatically handle scaling and normalization internally.
For best results, ensure your data has sufficient resolution in regions of interest.