发表机构
Youngstown State University; Kent State University; George Washington University（扬斯敦州立大学; 肯特州立大学; 乔治·华盛顿大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出量子结构化世界模型（QSWMs），研究量子启发结构对世界建模的作用，在基本元胞自动机上评估其变体，发现复值QSWM局部预测潜力良好但类密度矩阵变体存在长时序预测局限。
AI 中文摘要
世界模型学习能总结交互历史、随时间演化并支持预测、模拟或规划的潜在状态。现有多数世界模型用经典向量、概率分布、循环隐状态或Transformer激活表示这些状态。本文提出量子结构化世界模型（Quantum-Structured World Models，QSWMs），这是一种用于预测世界建模的量子启发框架，包含结构化潜在状态、潜在转移算子和受测量启发的解码映射。研究探讨量子理论启发的数学结构（如实值复表示、类密度矩阵的潜在变量）是否为世界建模提供有用的归纳偏置，确立了经典包含性、预测充分性和结构化紧凑性三个基础性质。随后实例化复值QSWM和类密度矩阵QSWM变体，在基本元胞自动机上与强经典基线对比评估。结果显示复值QSWM具有良好的局部预测潜力，同时也暴露了类密度矩阵变体在长时序滚动预测中的局限性。
英文摘要
World models learn latent states that summarize interaction histories, evolve over time, and support prediction, simulation, or planning. Most existing world models represent these states using classical vectors, probability distributions, recurrent hidden states, or transformer activations. In this paper, we introduce Quantum-Structured World Models (QSWMs), a quantum-inspired framework for predictive world modeling with structured latent states, latent transition operators, and measurement-inspired decoding maps. We study whether mathematical structures inspired by quantum theory, such as complex-valued representations and density-matrix-like latents, provide useful inductive biases for world modeling. We establish three foundational properties: classical inclusion, predictive sufficiency, and structured compactness. We then instantiate complex-valued and density-matrix-like QSWM variants and evaluate them on elementary cellular automata against strong classical baselines. Results show promising local predictive potential for complex-valued QSWMs, while also revealing limitations in long-horizon rollout, density-matrix variants
Comments19 pages, 5 figures,
