# Towards AutoQML: A Cloud-Based Automated Circuit Architecture Search Framework

arXiv: https://arxiv.org/abs/2202.08024 · HTML: https://mlsanity.com/paper/2202.08024/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Feb 2022.

## Built on by (most cited first, 7 of 7)

- [Hybrid quantum ResNet for car classification and its hyperparameter optimization](https://mlsanity.com/paper/2205.04878/) (2022)
- [AutoQML: Automated Quantum Machine Learning for Wi-Fi Integrated Sensing and Communications](https://mlsanity.com/paper/2205.09115/) (2022)
- [Toward Automated Quantum Variational Machine Learning](https://mlsanity.com/paper/2312.01567/) (2023)
- [quEEGNet: Quantum AI for Biosignal Processing](https://mlsanity.com/paper/2210.00864/) (2022)
- [On Optimizing Hyperparameters for Quantum Neural Networks](https://mlsanity.com/paper/2403.18579/) (2024)
- [Quantum Machine Learning hyperparameter search](https://mlsanity.com/paper/2302.10298/) (2023)
- [AutoQML: A Framework for Automated Quantum Machine Learning](https://mlsanity.com/paper/2502.21025/) (2025)

## Abstract (arXiv)

The learning process of classical machine learning algorithms is tuned by hyperparameters that need to be customized to best learn and generalize from an input dataset. In recent years, Quantum Machine Learning (QML) has been gaining traction as a possible application of quantum computing which may provide quantum advantage in the future. However, quantum versions of classical machine learning algorithms introduce a plethora of additional parameters and circuit variations that have their own intricacies in being tuned. In this work, we take the first steps towards Automated Quantum Machine Learning (AutoQML). We propose a concrete description of the problem, and then develop a classical-quantum hybrid cloud architecture that allows for parallelized hyperparameter exploration and model training. As an application use-case, we train a quantum Generative Adversarial neural Network (qGAN) to generate energy prices that follow a known historic data distribution. Such a QML model can be used for various applications in the energy economics sector.