Light as a Quantum Agent: Bridging Path Integrals and Reinforcement Learning

We propose a novel and interdisciplinary conceptual framework that bridges Feynman’s path integral formulation of quantum mechanics with reinforcement learning (RL) in artificial intelligence. In the path integral approach, a quantum system does not follow a single predetermined trajectory but instead explores all possible paths simultaneously, assigning each a complex amplitude weighted by e<sup>iS/h</sup>, where <I>S</I> represents the classical action. Constructive and destructive interference across these paths acts as a natural filter, amplifying trajectories of stationary action and suppressing suboptimal ones, thereby leaving paths of least action as the observable outcomes. We argue this process is strongly analogous to a quantum agent evaluating an entire policy space in superposition, where interference effectively encodes a reward mechanism that eliminates non-optimal policies. This perspective not only deepens our understanding of light’s propagation in complex refractive media but also inspires the design of quantum-inspired Reinforcement learning architectures capable of leveraging the intrinsic parallelism of quantum mechanics. Furthermore, the advent of quantum computing, with its inherent properties of superposition, entanglement, and quantum interference, provides a tangible pathway for implementing such algorithms in practice. To illustrate this paradigm, we propose a toy model wherein policies are encoded as quantum states, rewards are mapped to phase shifts, and measurement collapses the superposed state into an optimal policy. The implications of this framework extend beyond algorithmic innovation, offering insights into the possibility that nature itself operates as a quantum learning system, with physical laws emerging from a process akin to reinforcement learning.