AQMLATOR – AN AUTO QUANTUMMACHINE LEARNING E-PLATFORM

Abstract

A successful Machine Learning (ML) model implementation requires three
main components: training dataset, suitable model architecture and training
procedure. Given dataset and task, finding an appropriate model might be challenging.
AutoML, a branch of ML, focuses on automatic architecture search —
a meta method that aims at removing human from ML system design process.
The success of ML and the development of quantum computing (QC) in recent
years led to a birth of new fascinating field called Quantum Machine Learning
(QML) that, amongst others, incorporates quantum computers into ML models.
In this paper we present AQMLator, an Auto Quantum Machine Learning
platform that aims to automatically propose and train the quantum layers of
an ML model with minimal input from the user. 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.