Changelog

The separate build and learn agents have been replaced by one unified Coda agent for writing circuits and answering quantum computing questions.

• mode and fast are deprecated. Existing mode values remain accepted, but new integrations should omit both fields.
• budget (low, medium, or high; default: high) controls the tradeoff between response quality and speed.
• code_mode controls code delivery: inline streams code in token events, while file returns it in a files event before completion.

The experimental POST /search and POST /fetch endpoints have been removed from the Coda API.

The conductorquantum Python SDK now supports budget and code_mode, omits mode by default, and uses the canonical /tools/* and /qpus* endpoint paths.

Added a new batch endpoint for running coulomb-blockade-peak-detector-v2 over multiple traces in a single request.

• POST /models/batch and client.control.models.batch.run(...)
• Accepts a .npy or .pt file containing a 2D array shaped (batch_size, trace_length)
• Returns output.outputs ordered by batch index

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import numpy as np
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from conductorquantum import ConductorQuantum
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client = ConductorQuantum(
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    token="YOUR_TOKEN",
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)

Ising Calibration 1

NVIDIA Ising Calibration 1: 35B-parameter VLM that interprets quantum calibration plots and produces structured assessments for autonomous QPU tune-up.

• POST /agents/{agent_id} and client.control.agents.run("ising-calibration-v1", ...)
• GET /agents and client.control.agents.list()

Launched the Coda API for interacting with quantum hardware programmatically.

• GET /qpus: list available QPU devices
• POST /qpus: submit quantum circuits
• POST /qpus/status: check job status
• client.coda.qpus.list(), client.coda.qpus.run(...), client.coda.qpus.status(...)

Added coulomb-diamond-detector-v2 to the Control API. This version detects the vertices of Coulomb diamond structures directly from conductance data with improved accuracy over v1.

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result = client.control.models.run(
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    model="coulomb-diamond-detector-v2",
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    data=f,
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)

Added resonator-dip-finder-v0 to the Control API. Detects resonator dips in spectroscopy magnitude traces, accepting 1D input of any length and returning detected dip indices.

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result = client.control.models.run(
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    model="resonator-dip-finder-v0",
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    data=f,
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)

Vote on model results to help improve model quality.

• PUT /model-results/{result_id}/vote: create or update a vote
• DELETE /model-results/{result_id}/vote: remove a vote
• client.control.model_results.vote_on_model_result(result_id, vote=1)
• client.control.model_results.remove_vote_on_model_result(result_id)

Added coulomb-diamond-detector-v1 to the Control API. Unlike v0 which requires a pre-segmented binary mask, v1 detects diamond vertices directly from raw conductance data without a segmentation step.

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result = client.control.models.run(
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    model="coulomb-diamond-detector-v1",
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    data=f,
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)

Added two new model families to the Control API:

• Anticrossing Detector: detects anticrossing features in charge stability diagrams
• Electron Unload Detector: detects electron unloading events in gate voltage sweeps

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result = client.control.models.run(
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    model="anticrossing-detector-v0",
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    data=f,
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)

Added the Double Quantum Dot (DQD) Search routine to Stanza. Automates the search for double quantum dot regimes using adaptive grid search and barrier sweeps.

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from stanza.routines.builtins.dqd_search import DQDSearchRoutine
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routine = DQDSearchRoutine(device)
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routine.run()

Stream measurement data to interactive Bokeh plots during experiments with automatic axis labelling and configurable update rates.

Automated diagnostics to verify instrument connectivity, channel mappings, and basic qubit response before starting experiments.

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from stanza.routines.builtins.health_check import HealthCheckRoutine
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routine = HealthCheckRoutine(device)
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routine.run()

Initial release of Stanza, the open-source Python framework for quantum device control.

• Device configuration and channel mapping
• Instrument drivers (OPX, QDAC2)
• Data logging with HDF5 and JSONL writers
• Calibration routines framework

$
pip install stanza-quantum

Deployed charge-stability-diagram-binary-classifier-v0-16x16 to the Control API. A compact classifier designed for rapid double quantum dot search, accepting 16x16 input patches.

Deployed coulomb-diamond-segmenter-v0 and coulomb-diamond-detector-v0 to the Control API. Segment Coulomb diamond conductance grids and detect diamond vertex structures.

Deployed charge-stability-diagram-transition-detector-v0 to the Control API. Detects the locations of charge transition lines in 2D charge stability diagrams.

Added the first batch of quantum device characterization models to the Control API:

• Charge Stability Diagram: segmentation, binary classification, and transition detection models
• Coulomb Blockade: classifiers and peak detectors for current measurements
• Pinch-off: classifier and parameter extractor for pinch-off curves
• Turn-on: classifier and threshold voltage extractor for turn-on measurements

Launched the Control API and conductorquantum Python SDK for programmatic access to ML models for quantum device characterization.

• GET /models: list models
• POST /models: run a model
• GET /model-results: list results
• GET /model-results/{result_id}: fetch a result
• DELETE /model-results/{result_id}: delete a result