Quantum agent learns spacetime structure from information task
Spacetime Emergence in the Memory of a Learning Agent
Artificial IntelligenceMachine Learning
Summary
This work shows that a computer agent can learn about the structure of spacetime by interacting with quantum particles, without being told any physics rules directly. The agent uses reinforcement learning to predict outcomes and in doing so, builds an internal model that resembles how spacetime connects events through cause and effect. Essentially, concepts of distance and time emerge from the agent's successful learning and actions. This approach hints at how ideas of space and time might arise from quantum experiences themselves.
What this means in practice
- •For quantum software developers: Create AI agents that infer physical properties like spacetime geometry from quantum data without explicit physics inputs.
- •For quantum computing teams: Use learned causal structures to design new protocols for quantum communication relying on emergent spacetime concepts.
Authors
Philip A. LeMaitre, T. Rick Perche
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.