标题： 关于设计可表达且可训练的基于脉冲的量子机器学习模型 显示英文标题

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

摘要： 基于脉冲的量子机器学习（QML）由于其卓越的硬件效率，已成为量子人工智能中的新范式。 对于实际应用，基于脉冲的模型必须具有表达能力和可训练性。 先前的研究表明，在动态对称性下的基于脉冲的模型可以被有效训练，这是由于具有有利的损失景观，没有平坦区域。 然而，当模型设计不当时，由此产生的不可控性可能会损害表达能力。 本文研究了基于脉冲的QML模型在保持可训练性的前提下具备表达能力的要求。 我们提出了一种与系统的初始状态、测量可观测量和底层动力学对称性李代数相关的必要条件，并通过数值模拟进行了支持。 我们的研究结果建立了一个设计实用的基于脉冲的QML模型的框架，该框架平衡了表达能力和可训练性。 显示英文摘要

摘要： 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.