mlsanityData to 14 Oct 2026

Quantum-Structured World Models (QSWMs) for Predictive Latent Dynamics

arXiv 2608.05371 ↗First posted 5 Aug 2026cs.LGcited by 0

On the map: World models → Latent predictive world models

Abstract (arXiv)

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

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Latent predictive world models, one of 11 directions in World models. Builds on 18 of its 32 references that are in the map. First posted on arXiv 5 Aug 2026.

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18 of 32 references.

Closest ancestors · 1

Same direction, newest first

  1. 2018Learning Latent Dynamics for Planning from Pixels12 Nov

Other references · 17

  1. 2017Attention Is All You Needcited by 60,361
  2. 2014Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translationcited by 4,057
  3. 2019Dream to Control: Learning Behaviors by Latent Imaginationcited by 1,653
  4. 2018World Modelscited by 1,513
  5. 2019Mastering Atari, Go, Chess and Shogi by Planning with a Learned Modelcited by 1,353
  6. 2023Mastering Diverse Domains through World Modelscited by 1,253
  7. 2020Mastering Atari with Discrete World Modelscited by 1,007
  8. 2019Model-Based Reinforcement Learning for Ataricited by 502
  9. 2021Towards Out-Of-Distribution Generalization: A Surveycited by 436
  10. 2025AdaWorld: Learning Adaptable World Models with Latent Actionscited by 114
  11. 2025NavMorph: A Self-Evolving World Model for Vision-and-Language Navigation in Continuous Environmentscited by 45
  12. 2025Emergent Abilities in Large Language Models: A Surveycited by 37
5 more
  1. 2025LaDi-WM: A Latent Diffusion-based World Model for Predictive Manipulationcited by 24
  2. 2025Research Program: Theory of Learning in Dynamical Systemscited by 5
  3. 2025Readout-Side Bypass for Residual Hybrid Quantum-Classical Modelscited by 4
  4. 2026Latent State Design for World Models under Sufficiency Constraintscited by 3
  5. 2026On the Representational Limits of Quantum-Inspired 1024-D Document Embeddings: An Experimental Evaluation Frameworkcited by 1

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  5. 2026An Irreducible Quantum Advantage in Aligning World Models with Reality
  6. 2026An Empirical Study of World Model Quantization
  7. 2026QLAM: A Quantum Long-Attention Memory Approach to Long-Sequence Token Modeling
  8. 2025Cloning Deterministic Worlds: The Critical Role of Latent Geometry in Long-Horizon World Models