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 moving 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.

本研究解决了量子机器学习模型实现过程中模型架构选择的挑战。我们提出了AQMLator，一个自动量子机器学习平台，旨在通过最小的用户输入自动建议和训练量子层，使数据科学家能够更容易地使用量子计算。该平台使用标准机器学习库，便于集成到现有的机器学习流程中。

AQMLator -- 一个自动量子机器学习电子平台