The paper demonstrates that a model‑based reinforcement‑learning agent can learn the causal structure of Minkowski spacetime solely from maximizing quantum mutual information between localized probes, without explicit knowledge of the metric. During training, the agent builds an internal predictive model whose planner develops the null structure characteristic of spacetime, enabling accurate reconstruction of the causal structure. This result suggests that spacetime concepts can emerge from an agent’s operational quantum experience.
arXiv:2607. 09422v1 Announce Type: new Abstract: Cooperative multi-agent reinforcement learning is well suited to problems with large parameter spaces and exploitable local structure, such as the tuning of electrostatically-defined quantum-dot arrays.
arXiv:2608. 02826v1 Announce Type: cross Abstract: Reinforcement learning is a subfield of machine learning that studies how an agent interacts with an environment in order to extract as large a reward as possible.
Quantum Tiq‑Taq‑Toe is a popular benchmark for quantum computing and machine learning, yet no reinforcement learning (RL) methods have been applied to it. The paper introduces RL techniques for this game, which is simpler than Quantum Chess but still challenging due to partial observability and exponential state complexity. States are represented by a 3×3 measurement matrix and a 9×9 move‑history matrix of entanglement relations, making strategy development difficult because each move can collapse the quantum state.
arXiv:2608. 15715v1 Announce Type: cross Abstract: Quantum feedback control requires acting on noisy continuous measurement records without direct access to the underlying quantum state.
arXiv:2601. 08662v3 Announce Type: replace Abstract: This tutorial is designed to make reinforcement learning (RL) more accessible to undergraduate students by offering clear, example-driven explanations.
A single observation can admit several plausible futures, making uncertainty an essential part of world modelling. However, quantum generative models remain underexplored for this task. We introduce Q...
arXiv:2601. 10034v2 Announce Type: cross Abstract: Decision making often exhibits context dependence that challenges classical probability theory.
arXiv:2607. 00365v1 Announce Type: cross Abstract: Artificial intelligence (AI) and quantum information (QI) are rapidly co-evolving.
arXiv:2606. 18746v1 Announce Type: new Abstract: This paper develops a formal account of what generalist agents must store in memory in order to act near-optimally across multiple environments and goals.
arXiv:2511. 22226v2 Announce Type: replace Abstract: The standard theory of model-free reinforcement learning assumes that the environment dynamics are stationary and that agents are decoupled from their environment, such that policies are treated as being separate from the world they inhabit.
arXiv:2608. 19779v1 Announce Type: cross Abstract: World models provide digital simulacra of the true world, allowing agents to be trained and tested before costly real-world deployment.
arXiv:2608. 05371v1 Announce Type: new Abstract: World models learn latent states that summarize interaction histories, evolve over time, and support prediction, simulation, or planning.