量子構造世界モデル（QSWM）による予測的潜在ダイナミクス
Quantum-Structured World Models (QSWMs) for Predictive Latent Dynamics
量子理論に着想を得た複素数表現や密度行列型の潜在状態を用いた世界モデルを提案し、セルオートマトン上で古典的ベースラインと比較して予測性能を評価した。
著者: Hailong Jiang, Emran Hossain, Feng Yu, Jianfeng Zhu, Guilin Zhang, Wulan Guo
分類: cs.LG
原文アブストラクト
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