Overview | Conductor Quantum

Machine learning models for automated analysis of quantum device data. Models power the analysis step of Control’s calibration loop, replacing hours of manual interpretation with structured, automated results. Use them during device tune-up and characterization to quickly extract key device parameters from your measurement data.

Regardless of the specific model, the SDK always wraps the response in a ModelResultInfo object:

Field	Type	Description
`id`	`str`	Unique identifier for the execution
`model`	`str`	Name of the executed model
`created_at`	`datetime`	Timestamp (UTC) when the execution was created
`input_file_name`	`str`	Name of the uploaded input file
`input_file_size`	`int`	Size of the input file in bytes
`output`	`Dict`	Model‑specific output (boolean or dictionary)

Model Versioning

Some of our models use a versioning scheme (v0, v1, etc.) where higher numbers generally indicate improved accuracy and performance. When selecting a model version, consider:

Newer versions typically offer better accuracy and robustness
Specialized versions for specific data types or conditions
Performance variations based on your measurement characteristics

We recommend testing multiple versions to find the optimal one for your quantum device data.

Charge Stability Diagram Analysis

Model	Description	Input Shape	Output Keys	Endpoints
`charge-stability-diagram-segmenter-v0`	Segments a charge stability diagram into different regions	(128, 128)	`segmentation`	Run
`charge-stability-diagram-binary-classifier-v0-16x16`	Classifies a charge stability diagram as no dot, single, or double dot	(16, 16)	`classification`	Run
`charge-stability-diagram-binary-classifier-v1-16x16`	Classifies a charge stability diagram as no dot, single, or double dot	(16, 16)	`classification`	Run
`charge-stability-diagram-binary-classifier-v2-16x16`	Classifies a charge stability diagram as no dot, single, or double dot	(16, 16)	`classification`	Run
`charge-stability-diagram-binary-classifier-v3-16x16`	Classifies a charge stability diagram as no dot, single, or double dot	(16, 16)	`classification`, `score`	Run
`charge-stability-diagram-binary-classifier-v0-48x48`	Classifies a charge stability diagram as no dot, single, or double dot	(48, 48)	`classification`, `score`	Run
`charge-stability-diagram-binary-classifier-v1-48x48`	Classifies a charge stability diagram as no dot, single, or double dot	(48, 48)	`classification`, `score`	Run
`charge-stability-diagram-transition-detector-v0`	Detects the locations of charge transition lines in a charge stability diagram	(n, m)	`transition_lines`	Run
`charge-stability-diagram-transition-detector-v1`	Detects the locations of charge transition lines in a charge stability diagram	(n, m)	`transition_lines`	Run
`charge-stability-diagram-transition-detector-v2`	Detects the locations of charge transition lines in a charge stability diagram	(n, m)	`transition_lines`	Run
`charge-stability-diagram-transition-detector-v3`	Detects the locations of charge transition lines in a charge stability diagram	(n, m)	`transition_lines`	Run

You can see how to use the charge stability diagram models on the Charge Stability Diagram Models page.

Coulomb Blockade Analysis

Model	Description	Input Shape	Output Keys	Endpoints
`coulomb-blockade-classifier-v0`	Determines if a current measurement exhibits Coulomb blockade characteristics	(n, )	`classification`	Run
`coulomb-blockade-classifier-v1`	Determines if a current measurement exhibits Coulomb blockade characteristics	(n, )	`classification`	Run
`coulomb-blockade-classifier-v2`	Determines if a current measurement exhibits Coulomb blockade characteristics	(128, )	`classification`, `score`	Run
`coulomb-blockade-classifier-v3`	Determines if a current measurement exhibits Coulomb blockade characteristics	(128, )	`classification`, `score`	Run
`coulomb-blockade-peak-detector-v0`	Detects the locations of Coulomb blockade peaks in current measurement	(n, )	`peak_indices`	Run
`coulomb-blockade-peak-detector-v1`	Detects the locations of Coulomb blockade peaks in current measurement	(n, )	`peak_indices`	Run
`coulomb-blockade-peak-detector-v1-mini`	Detects the locations of Coulomb blockade peaks in current measurement	(n, )	`peak_indices`	Run
`coulomb-blockade-peak-detector-v2`	Detects the locations of Coulomb blockade peaks in current measurement	(n, )	`peak_indices`	Run

You can see how to use the Coulomb Blockade models on the Coulomb Blockade Models page.

Note: coulomb-blockade-peak-detector-v1-mini is a quicker version of coulomb-blockade-peak-detector-v1 that gives a good trade-off between accuracy and speed.

Coulomb Diamond Analysis

Model	Description	Input Shape	Output Keys	Endpoints
`coulomb-diamond-segmenter-v0`	Segments a Coulomb diamond conductance grid to Coulomb diamond regions	(n, m)	`segmentation`	Run
`coulomb-diamond-detector-v0`	Detects the vertices of Coulomb diamond structures from a binary mask	(n, m)	`diamond_vertices`	Run
`coulomb-diamond-detector-v1`	Detects the vertices of Coulomb diamond structures directly from conductance data	(n, n)	`diamond_vertices`	Run
`coulomb-diamond-detector-v2`	Detects the vertices of Coulomb diamond structures directly from conductance data	(n, n)	`diamond_vertices`	Run

You can see how to use the Coulomb Diamond models on the Coulomb Diamond Models page.

Pinch-off Analysis

Model	Description	Input Shape	Output Keys	Endpoints
`pinch-off-classifier-v0`	Determines if a current measurement exhibits pinch-off characteristics	(n, )	`classification`	Run
`pinch-off-parameter-extractor-v0`	Extracts key parameters from pinch-off measurements (cut_off_voltage, transition_voltage, saturation_voltage)	(n, )	`cut_off_index`, `transition_index`, `saturation_index`	Run

You can see how to use the Pinch-off models on the Pinch-off Models page.

Turn-on Analysis

Model	Description	Input Shape	Output Keys	Endpoints
`turn-on-classifier-v0`	Determines if a current measurement exhibits turn-on characteristics	(n, )	`classification`	Run
`turn-on-parameter-extractor-v0`	Extracts the threshold voltage from turn-on measurements	(n, )	`threshold_index`	Run

You can see how to use the Turn-on models on the Turn-on Models page.

Resonator Spectroscopy Analysis

Model	Description	Input Shape	Output Keys	Endpoints
`resonator-dip-finder-v0`	Detects resonator dips in spectroscopy magnitude traces	(n,)	`dip_indices`	Run

You can see how to use the Resonator Dip Finder model on the Resonator Dip Finder page.

QEC Decoding (NVIDIA Ising Decoding)

Model	Description	Input Shape	Output Keys	Endpoints
`ising-decoding-v1-fast`	Low-latency AI pre-decoder for surface code QEC (0.9M params, R=9)	(batch, 4, T, D, D)	`logits`, `variant`, `batch_size`	Run
`ising-decoding-v1-accurate`	Higher-accuracy AI pre-decoder for surface code QEC (1.8M params, R=13)	(batch, 4, T, D, D)	`logits`, `variant`, `batch_size`	Run

These models accept detector syndrome tensors as 5D NumPy arrays and return correction logits. See the QEC Decoding page for details and code examples.

Agents

Agent	Description	Input	Endpoints
`ising-calibration-v1`	Analyses quantum calibration plots using the NVIDIA Ising Calibration VLM	`image_base64`, `prompt` (optional)	Run

See the NVIDIA Ising Calibration page for details and code examples.