AutoQML自动化构建量子机器学习流程，降低使用门槛。
AutoQML: A Framework for Automated Quantum Machine Learning
- 将自动机器学习方法适配到量子机器学习，统一编程接口
- 在4个工业场景中生成性能媲美经典模型和人工设计的量子方案
- 基于sQUlearn库支持多种算法，适合想快速验证量子学习的开发者
自动化机器学习（AutoML）通过自动优化超参数和构建流水线，显著提升了机器学习软件开发效率，减少了人工干预。量子机器学习（QML）利用量子计算有望超越经典机器学习能力，但其复杂性带来较高入门门槛。本文提出 extit{AutoQML}，一个将AutoML方法应用于QML的新框架，提供模块化且统一的编程接口，促进QML流水线开发。该框架基于QML库sQUlearn，支持多种QML算法。AutoQML可构建面向监督学习任务的端到端流水线，确保易用性与有效性。我们在四个工业应用场景中评估了AutoQML，结果表明其能生成高性能的QML流水线，性能与经典机器学习模型及人工设计的量子方案相当。
原文摘要 · Abstract (English)
Automated Machine Learning (AutoML) has significantly advanced the efficiency of ML-focused software development by automating hyperparameter optimization and pipeline construction, reducing the need for manual intervention. Quantum Machine Learning (QML) offers the potential to surpass classical machine learning (ML) capabilities by utilizing quantum computing. However, the complexity of QML presents substantial entry barriers. We introduce \emph{AutoQML}, a novel framework that adapts the AutoML approach to QML, providing a modular and unified programming interface to facilitate the development of QML pipelines. AutoQML leverages the QML library sQUlearn to support a variety of QML algorithms. The framework is capable of constructing end-to-end pipelines for supervised learning tasks, ensuring accessibility and efficacy. We evaluate AutoQML across four industrial use cases, demonstrating its ability to generate high-performing QML pipelines that are competitive with both classical ML models and manually crafted quantum solutions.
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