自动设计量子机器学习模型，降低使用门槛。
AQMLator -- An Auto Quantum Machine Learning E-Platform
- 基于AutoML思想自动搜索量子层结构
- 仅需少量输入即可完成量子模型训练
- 兼容主流ML框架，适合初学者快速上手
成功的机器学习模型实现需要三个核心要素：训练数据集、合适的模型架构和训练流程。给定数据集和任务后，选择合适模型可能极具挑战性。AutoML作为机器学习的一个分支，专注于自动化架构搜索——一种将人类从机器学习系统设计中解放出来的元方法。近年来，机器学习的成功与量子计算的发展催生了一个新兴且引人注目的领域：量子机器学习（QML），其核心是将量子计算机融入机器学习模型。本文提出AQMLator，一个自动量子机器学习平台，旨在以最少的用户输入自动提出并训练机器学习模型中的量子层。该平台使数据科学家能够绕过量子计算的入门壁垒，轻松应用量子机器学习。AQMLator采用标准机器学习库，便于集成到现有机器学习工作流中。
原文摘要 · Abstract (English)
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.
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