AutoQML: A Framework for Automated Quantum Machine Learning

• URL: http://arxiv.org/abs/2502.21025v1
• Date: Fri, 28 Feb 2025 13:08:15 GMT
• Title: AutoQML: A Framework for Automated Quantum Machine Learning
• Authors: Marco Roth, David A. Kreplin, Daniel Basilewitsch, João F. Bravo, Dennis Klau, Milan Marinov, Daniel Pranjic, Horst Stuehler, Moritz Willmann, Marc-André Zöller,
• Abstract summary: We introduce emphAutoQML, a novel framework that adapts the AutoML approach to Quantum Machine Learning.<n>We evaluate AutoQML across four industrial use cases, demonstrating its ability to generate high-performing QML pipelines.
• Score: 0.44138069832935184
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Quantum Machine Learning: A Hands-on Tutorial for Machine Learning Practitioners and Researchers [51.03113410951073]
  This tutorial introduces readers with a background in AI to quantum machine learning (QML)<n>For self-consistency, this tutorial covers foundational principles, representative QML algorithms, their potential applications, and critical aspects such as trainability, generalization, and computational complexity.
  arXiv  Detail & Related papers  (2025-02-03T08:33:44Z)
• LLM-AutoDiff: Auto-Differentiate Any LLM Workflow [58.56731133392544]
  We introduce LLM-AutoDiff: a novel framework for Automatic Prompt Engineering (APE)<n>LLMs-AutoDiff treats each textual input as a trainable parameter and uses a frozen backward engine to generate feedback-akin to textual gradients.<n>It consistently outperforms existing textual gradient baselines in both accuracy and training cost.
  arXiv  Detail & Related papers  (2025-01-28T03:18:48Z)
• AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML [56.565200973244146]
  Automated machine learning (AutoML) accelerates AI development by automating tasks in the development pipeline. Recent works have started exploiting large language models (LLM) to lessen such burden. This paper proposes AutoML-Agent, a novel multi-agent framework tailored for full-pipeline AutoML.
  arXiv  Detail & Related papers  (2024-10-03T20:01:09Z)
• AQMLator -- An Auto Quantum Machine Learning E-Platform [0.0]
  AQMLator aims to automatically propose and train the quantum layers of an ML model with minimal input from the user. It uses standard ML libraries, making it easy to introduce into existing ML pipelines.
  arXiv  Detail & Related papers  (2024-09-26T23:23:27Z)
• Quantum Machine Learning Architecture Search via Deep Reinforcement Learning [8.546707309430593]
  We introduce deep reinforcement learning to explore proficient QML model architectures tailored for supervised learning tasks. Our methodology involves training an RL agent to devise policies that facilitate the discovery of QML models without predetermined ansatz. Our proposed method successfully identifies VQC architectures capable of achieving high classification accuracy while minimizing gate depth.
  arXiv  Detail & Related papers  (2024-07-29T16:20:51Z)
• Verbalized Machine Learning: Revisiting Machine Learning with Language Models [63.10391314749408]
  We introduce the framework of verbalized machine learning (VML) VML constrains the parameter space to be human-interpretable natural language. We empirically verify the effectiveness of VML, and hope that VML can serve as a stepping stone to stronger interpretability.
  arXiv  Detail & Related papers  (2024-06-06T17:59:56Z)
• Position: A Call to Action for a Human-Centered AutoML Paradigm [83.78883610871867]
  Automated machine learning (AutoML) was formed around the fundamental objectives of automatically and efficiently configuring machine learning (ML) We argue that a key to unlocking AutoML's full potential lies in addressing the currently underexplored aspect of user interaction with AutoML systems.
  arXiv  Detail & Related papers  (2024-06-05T15:05:24Z)
• LLMC: Benchmarking Large Language Model Quantization with a Versatile Compression Toolkit [55.73370804397226]
  Quantization, a key compression technique, can effectively mitigate these demands by compressing and accelerating large language models. We present LLMC, a plug-and-play compression toolkit, to fairly and systematically explore the impact of quantization. Powered by this versatile toolkit, our benchmark covers three key aspects: calibration data, algorithms (three strategies), and data formats.
  arXiv  Detail & Related papers  (2024-05-09T11:49:05Z)
• A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Ahead [5.629434388963902]
  Quantum Computing (QC) claims to improve the efficiency of solving complex problems, compared to classical computing. When QC is integrated with Machine Learning (ML), it creates a Quantum Machine Learning (QML) system. This paper aims to provide a thorough understanding of the foundational concepts of QC and its notable advantages over classical computing.
  arXiv  Detail & Related papers  (2023-10-16T11:52:54Z)
• Bringing Quantum Algorithms to Automated Machine Learning: A Systematic Review of AutoML Frameworks Regarding Extensibility for QML Algorithms [1.4469725791865982]
  This work describes the selection approach and analysis of existing AutoML frameworks regarding their capability of incorporating Quantum Machine Learning (QML) algorithms. For that, available open-source tools are condensed into a market overview and suitable frameworks are systematically selected on a multi-phase, multi-criteria approach. We build an extended Automated Quantum Machine Learning (AutoQML) framework with QC-specific pipeline steps and decision characteristics for hardware and software constraints.
  arXiv  Detail & Related papers  (2023-10-06T13:21:16Z)
• Benchmarking Automated Machine Learning Methods for Price Forecasting Applications [58.720142291102135]
  We show the possibility of substituting manually created ML pipelines with automated machine learning (AutoML) solutions. Based on the CRISP-DM process, we split the manual ML pipeline into a machine learning and non-machine learning part. We show in a case study for the industrial use case of price forecasting, that domain knowledge combined with AutoML can weaken the dependence on ML experts.
  arXiv  Detail & Related papers  (2023-04-28T10:27:38Z)
• Automatic Componentwise Boosting: An Interpretable AutoML System [1.1709030738577393]
  We propose an AutoML system that constructs an interpretable additive model that can be fitted using a highly scalable componentwise boosting algorithm. Our system provides tools for easy model interpretation such as visualizing partial effects and pairwise interactions. Despite its restriction to an interpretable model space, our system is competitive in terms of predictive performance on most data sets.
  arXiv  Detail & Related papers  (2021-09-12T18:34:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.