Developer builds open-source AutoML tool combining quantum and classical ML models

A PhD student at Western Michigan University has released QuOptuna, an open-source AutoML tool that runs hyperparameter searches across 17 quantum and 4 classical classifiers in a single unified workflow. The tool addresses key pain points in quantum machine learning, including manual circuit writing, lack of hyperparameter search standards, and absent fairness auditing. Users can launch a full web application with one terminal command, accessing a six-step optimization wizard, REST API, and live trial monitoring without additional installation steps. QuOptuna integrates fairness constraints directly into the optimization loop using fairlearn metrics, and generates SHAP-based explanations for the winning model. An optional two-agent LLM pipeline can automatically draft and review a research report, though all charts and metrics are available without it.