Speakers
Description
World models allow reinforcement-learning agents to evaluate actions in simulation before acting. For planning, however, reproducing typical simulated trajectories is not enough: the model must sample futures conditioned on a specified history and candidate action. This distinction is critical in safety settings, where a rare history, such as a pedestrian suddenly entering the road, can determine whether an otherwise successful policy acts catastrophically. Here we investigate whether quantum memory can improve such conditional world modeling. We formulate quantum world models as finite-dimensional conditional quantum simulators for planning, and introduce a quantum bisimulation distance that turns errors between memory states into guarantees on values and action-values. We then construct a controlled task in which the rare histories relevant for control have conditional futures that cannot be represented exactly by any finite classical world model. Consequently, every finite classical world model incurs an action-value error bounded below by a constant independent of its number of memory states. In contrast, a single qutrit samples the required conditional futures exactly, yielding zero planning error for every policy and for optimal control. Our results show that finite quantum memory can provide exact conditional simulators for reinforcement-learning tasks beyond the reach of finite classical memory.
| Topic | Theoretical foundations of quantum learning |
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| Additional Topic | Encoding and processing of data in quantum systems |