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1 2025

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AQMLator – an auto quantum machine-learning e-platform

Rybotycki Tomasz, Gawron Piotr

Computer Science

2025

T. 26 (SI)

29--43

The successful implementation of a machine-learning (ML) model requires three main components: a training data set, a suitable model architecture, and a suitable training procedure. Given the data set and task, finding an appropriate model might be challenging. AutoML, a branch of ML, focuses on an automatic architecture search – a meta method that aims to remove the need for human interaction with the ML system-design process. The success of ML and the development of quantum computing (QC) in recent years has led to the birth of a new fascinating field called quantum machine learning (QML), which incorporates quantum computers into ML models (among other things). In this paper, we present AQMLator, an auto quantum machine-learning platform that aimsto automatically propose and train the quantum layers of an ML model with minimal input from the user. In this way, data scientists can bypass the entry barrier for QC and use QML. AQMLator uses standard ML libraries, making it easy to introduce into existing ML pipelines.