Trainability issues in quantum policy gradients

IF 6.3 2区 物理与天体物理 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
André Sequeira, Luis Paulo Santos and Luis Soares Barbosa
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引用次数: 0

Abstract

This research explores the trainability of Parameterized Quantum Circuit-based policies in Reinforcement Learning, an area that has recently seen a surge in empirical exploration. While some studies suggest improved sample complexity using quantum gradient estimation, the efficient trainability of these policies remains an open question. Our findings reveal significant challenges, including standard Barren Plateaus with exponentially small gradients and gradient explosion. These phenomena depend on the type of basis-state partitioning and the mapping of these partitions onto actions. For a polynomial number of actions, a trainable window can be ensured with a polynomial number of measurements if a contiguous-like partitioning of basis-states is employed. These results are empirically validated in a multi-armed bandit environment.
量子政策梯度的可训练性问题
本研究探讨了强化学习中基于参数化量子电路的策略的可训练性,这一领域的实证探索最近出现了激增。虽然一些研究表明,量子梯度估计提高了样本复杂度,但这些策略的高效可训练性仍是一个未决问题。我们的研究结果揭示了巨大的挑战,包括梯度呈指数级小的标准贫瘠高原和梯度爆炸。这些现象取决于基态划分的类型以及这些划分对行动的映射。对于多项式数量的动作,如果采用类似连续的基态划分,则只需多项式数量的测量就能确保可训练窗口。这些结果在多臂强盗环境中得到了经验验证。
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来源期刊
Machine Learning Science and Technology
Machine Learning Science and Technology Computer Science-Artificial Intelligence
CiteScore
9.10
自引率
4.40%
发文量
86
审稿时长
5 weeks
期刊介绍: Machine Learning Science and Technology is a multidisciplinary open access journal that bridges the application of machine learning across the sciences with advances in machine learning methods and theory as motivated by physical insights. Specifically, articles must fall into one of the following categories: advance the state of machine learning-driven applications in the sciences or make conceptual, methodological or theoretical advances in machine learning with applications to, inspiration from, or motivated by scientific problems.
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