用量子启发结构建模世界状态，提升预测能力。
Quantum-Structured World Models (QSWMs) for Predictive Latent Dynamics
- 引入复数与密度矩阵结构的潜变量表示。
- 在细胞自动机上局部预测表现优于经典模型。
- 适合研究新型潜空间结构与长期预测的学者。
世界模型通过潜状态总结交互历史，随时间演化并支持预测、模拟或规划。现有模型多采用经典向量、概率分布、循环隐藏状态或Transformer激活值表示潜状态。本文提出量子结构化世界模型（QSWMs），一种基于量子理论启发的框架，包含结构化潜状态、潜转移算子和测量式解码映射。研究了复数表示与类密度矩阵潜变量等量子数学结构是否为世界建模提供有益归纳偏置。建立了三个基础性质：经典包含性、预测充分性与结构紧凑性。进一步实例化复数型与类密度矩阵型QSWM，并在基础细胞自动机任务上与强基准模型对比。结果表明，复数型QSWM在局部预测上表现优异，但长时程推演仍存局限，类密度矩阵变体亦存在类似问题。
原文摘要 · Abstract (English)
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
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。