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

2026-08-05

AI Summary

This work addresses the challenge of effectively modeling structured temporal dependencies in complex dynamical systems, which traditional world models struggle to capture. The authors propose Quantum-inspired State-space World Models (QSWMs), introducing for the first time complex-valued representations and density-matrix-like structures from quantum theory into latent state modeling. QSWMs integrate complex-valued neural networks, density-matrix-analogous latent variables, structured transition operators, and measurement-based decoding mechanisms to establish a novel inductive bias that ensures classical inclusiveness, predictive sufficiency, and structural compactness. Evaluated on elementary cellular automata tasks, QSWMs demonstrate superior local prediction performance, confirming their modeling potential, although challenges remain in long-horizon rollouts and variants leveraging full density matrix formalism.

Abstract

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

Problem

• Quantum-Structured World Models
• predictive latent dynamics
• inductive biases
• complex-valued representations
• density-matrix-like latents

Innovation

• Quantum-Structured World Models
• complex-valued representations
• density-matrix latents
• predictive world modeling
• inductive bias