Decision Making Photonics: Solving Bandit Problems Using Photons

Makoto Naruse

doi:10.1109/JSTQE.2019.2929217

Decision Making Photonics: Solving Bandit Problems Using Photons

2020-01-01

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DOI：10.1109/JSTQE.2019.2929217

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Decision making in dynamically changing uncertain environments is one of the most important elements in information and communications technology, in applications ranging from resource assignment in data centers to wireless communications and search functions. Here, we review our research on the efficient physical realization or acceleration of decision making using photonics. The problem of interest is the multi-armed bandit (MAB) problem, where the purpose is to maximize the total rewards in unknown environments that involve difficult tradeoffs in the so-called exploration-exploitation dilemma. We describe the principle of solving MAB problems by utilizing the wave-particle duality of single photons in which the probabilistic attributes of single light quanta are employed for exploration. The principle is transformed into ultrafast laser chaos, where the chaotically oscillating irregular time series provides fast and scalable decision making abilities. The problem becomes even more difficult when multiple players are involved, in the so-called competitive MAB (CMAB) problem, where the expected value is related to maximizing the social benefit and ensuring fairness among individuals. We demonstrate that entangled photons can resolve the CMAB problem. Theoretical studies on photonic decision making are also reviewed, showing that six entities in total interact with each other according to an octahedral structure.

Keywords:

Decision making

photonics

reinforcement learning

artificial intelligence

multi-armed bandit problem

single photon

laser chaos

entangled photons

categorical system model

Decision Making Photonics: Solving Bandit Problems Using Photons

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