On the Design of Expressive and Trainable Pulse-based Quantum Machine Learning Models

• URL: http://arxiv.org/abs/2508.05559v1
• Date: Thu, 07 Aug 2025 16:40:09 GMT
• Title: On the Design of Expressive and Trainable Pulse-based Quantum Machine Learning Models
• Authors: Han-Xiao Tao, Xin Wang, Re-Bing Wu,
• Abstract summary: Pulse-based Quantum Machine Learning (QML) has emerged as a novel paradigm in quantum artificial intelligence.<n>For practical applications, pulse-based models must be both expressive and trainable.<n>This paper investigates the requirements for pulse-based QML models to be expressive while preserving trainability.
• Score: 4.852613028421959
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Pulse-based Quantum Machine Learning (QML) has emerged as a novel paradigm in quantum artificial intelligence due to its exceptional hardware efficiency. For practical applications, pulse-based models must be both expressive and trainable. Previous studies suggest that pulse-based models under dynamic symmetry can be effectively trained, thanks to a favorable loss landscape that has no barren plateaus. However, the resulting uncontrollability may compromise expressivity when the model is inadequately designed. This paper investigates the requirements for pulse-based QML models to be expressive while preserving trainability. We present a necessary condition pertaining to the system's initial state, the measurement observable, and the underlying dynamical symmetry Lie algebra, supported by numerical simulations. Our findings establish a framework for designing practical pulse-based QML models that balance expressivity and trainability.

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