Abstract
The last decade has seen an unprecedented growth in artificial intelligence and photonic technologies, both of which drive the limits of modern-day computing devices. In line with these recent developments, this work brings together the state of the art of both fields within the framework of reinforcement learning. We present the blueprint for a photonic implementation of an active learning machine incorporating contemporary algorithms such as SARSA, Q-learning, and projective simulation. We numerically investigate its performance within typical reinforcement learning environments, showing that realistic levels of experimental noise can be tolerated or even be beneficial for the learning process. Remarkably, the architecture itself enables mechanisms of abstraction and generalization, two features which are often considered key ingredients for artificial intelligence. The proposed architecture, based on single-photon evolution on a mesh of tunable beamsplitters, is simple, scalable, and a first integration in quantum optical experiments appears to be within the reach of near-term technology.
Highlights
Modern computing devices are rapidly evolving from handy resources to autonomous machines [1]
We present the blueprint for a photonic implementation of an active learning the work, journal citation and DOI
We numerically investigate its performance within typical reinforcement learning environments, showing that realistic levels of experimental noise can be tolerated or even be beneficial for the learning process
Summary
Modern computing devices are rapidly evolving from handy resources to autonomous machines [1]. In the wake of this technological progress, neuromorphic engineering [15] was developed to mimic neuro-biological systems on application-specific integrated circuits (ASIC) [16] Their improved performance is rooted in the parallelized operation and in the absence of a clear separation between memory and processing unit, which eliminates off-circuit data transfers. The processing unit, characterized by χ-values, adapts the agents behavior according to a specific update rule in order to maximize the expected, future reward within a given environment This unit can be implemented on an integrated photonic circuit. Since the architecture uses single photons, decision-making is fueled by genuine quantum randomness This feature marks a fundamental departure from pseudorandom number generation in conventional devices.
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