For AI agents: a documentation index is available at the root level at /llms.txt and /llms-full.txt. Append /llms.txt to any URL for a page-level index, or .md for the markdown version of any page.
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
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from dotenv import load_dotenv
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import os
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from conductorquantum import ConductorQuantum
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# Load API key from .env file
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load_dotenv()
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TOKEN = os.getenv("CONDUCTOR_API_KEY")
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# Initialize client
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client = ConductorQuantum(token=TOKEN)
Or provide the API key directly:
Python
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from conductorquantum import ConductorQuantum
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# Initialize client with API key
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client = ConductorQuantum(token="YOUR_API_KEY")
Usage Examples
Using the Coulomb Blockade Classifier
The Coulomb Blockade Classifier is a model that can classify a given current measurement as either
in the Coulomb Blockade regime or not.
You can download an example file to follow along with the example:
Python
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from dotenv import load_dotenv
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import os
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import numpy as np
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from conductorquantum import ConductorQuantum
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# Load API key from .env file
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load_dotenv()
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TOKEN = os.getenv("CONDUCTOR_API_KEY")
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# Initialize client
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client = ConductorQuantum(token=TOKEN)
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# Load Coulomb blockade data (current measurement)
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data = np.load("coulomb-blockade-classifier-v0.npy") # shape (n, )
Similarly, for v1 of the Coulomb Blockade Peak Detector, you can download an example file and replace the model name in the example above with coulomb-blockade-peak-detector-v1, and you should see the following output:
v1 is typically more accurate than v0 for larger sweep sizes of one hundred or more points.
Important Notes for Coulomb Blockade 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 Coulomb Blockade phenomena.
The peak detector outputs a list of indices corresponding to the detected peak locations in the input array.
Model versions: Higher version numbers typically indicate improved accuracy and performance. For example, v1 of the peak detector is typically more accurate than v0 for larger sweep sizes (one hundred or more points). Check the models overview for the latest available versions.