Using the Pinch-Off Models

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.control.models.run(
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.control.models.run(
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(data, 'k-')
5 plt.axvline(x=result.output["cut_off_index"], color='b', linestyle='--', label='Cutoff Index')
6 plt.axvline(x=result.output["transition_index"], color='g', linestyle='--', label='Transition Index')
7 plt.axvline(x=result.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()

Important Notes for Pinch-Off Models

Input dimensions: Input dimensions should ideally match the specified resolution of the model for the best results. You may need to interpolate or downsample your data to match the required shape. You can find the required input shapes for each model on the models overview page.
Data format: Input data should be a 1D numpy array representing current measurements as a function of gate voltage.
Output format:
- The classifier outputs a boolean value (True or False) indicating whether the measurement exhibits pinch-off behavior.
- The parameter extractor outputs a dictionary with three indices: cut_off_index, transition_index, and saturation_index, which correspond to key points in the pinch-off curve.
Model versions: Higher version numbers typically indicate improved accuracy and performance. Check the models overview for the latest available versions.

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.control.models.run(
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.control.models.run(
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(data, 'k-')
5 plt.axvline(x=result.output["cut_off_index"], color='b', linestyle='--', label='Cutoff Index')
6 plt.axvline(x=result.output["transition_index"], color='g', linestyle='--', label='Transition Index')
7 plt.axvline(x=result.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()

Important Notes for Pinch-Off Models

Input dimensions: Input dimensions should ideally match the specified resolution of the model for the best results. You may need to interpolate or downsample your data to match the required shape. You can find the required input shapes for each model on the models overview page.
Data format: Input data should be a 1D numpy array representing current measurements as a function of gate voltage.
Output format:
- The classifier outputs a boolean value (True or False) indicating whether the measurement exhibits pinch-off behavior.
- The parameter extractor outputs a dictionary with three indices: cut_off_index, transition_index, and saturation_index, which correspond to key points in the pinch-off curve.
Model versions: Higher version numbers typically indicate improved accuracy and performance. Check the models overview for the latest available versions.

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)

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

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.control.models.run(
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}")

1	import matplotlib.pyplot as plt
2
3	plt.figure(figsize=(10, 5), dpi=300)
4	plt.plot(data, 'k-')
5	plt.axvline(x=result.output["cut_off_index"], color='b', linestyle='--', label='Cutoff Index')
6	plt.axvline(x=result.output["transition_index"], color='g', linestyle='--', label='Transition Index')
7	plt.axvline(x=result.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()