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This paper trains a model-based reinforcement-learning agent on a quantum information task in Minkowski spacetime, and reports that its learned internal model comes to encode the light-cone (null) structure of the metric without being given it. The result is a proof-of-concept in a simple setting, not a general claim that spacetime emerges from learning. For AI and quantum readers, the useful takeaway is the method: probing what a learned world model represents by checking its causal structure against known physics.
Spacetime Emergence in the Memory of a Learning Agent
arXiv:2611.03596v1 Announce Type: cross Abstract: We show that the causal structure of spacetime can emerge within the memory of a learning agent performing a quantum information task. We train a model-based reinforcement-learning agent to maximize the quantum mutual information between localized quantum probes interacting with a quantum field in Minkowski spacetime, without directly providing it with information regarding the spacetime metric or causal structure. In learning to accomplish this task, the reinforcement-learning agent constructs an internal predictive model whose planner develops the characteristic null structure of spacetime, allowing us to reconstruct the causal structure of the Minkowski metric with high accuracy. Within this task, spacetime geometry is obtained from an agent's successful predictions and actions, providing a first step toward showing that spacetime concepts emerge from operational quantum experience.
Abstract:We show that the causal structure of spacetime can emerge within the memory of a learning agent performing a quantum information task. We train a model-based reinforcement-learning agent to maximize the quantum mutual information between localized quantum probes interacting with a quantum field in Minkowski spacetime, without directly providing it with information regarding the spacetime metric or causal structure. In learning to accomplish this task, the reinforcement-learning agent constructs an internal predictive model whose planner develops the characteristic null structure of spacetime, allowing us to reconstruct the causal structure of the Minkowski metric with high accuracy. Within this task, spacetime geometry is obtained from an agent's successful predictions and actions, providing a first step toward showing that spacetime concepts emerge from operational quantum experience.
Quantum Physics (quant-ph); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); General Relativity and Quantum Cosmology (gr-qc)
arXiv:2611.03596 [quant-ph]
(or arXiv:2611.03596v1 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2611.03596 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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