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arXiv · 2508.05559

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

Han-Xiao Tao·Xin Wang·Re-Bing Wu
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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 avoids 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 establish 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 provide a framework for designing practical pulse-based QML models that balance expressivity and trainability.

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Han-Xiao Tao, Xin Wang, Re-Bing Wu. 2025-08-07. On the Design of Expressive and Trainable Pulse-based Quantum Machine Learning Models. https://arxiv.org/abs/2508.05559

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