# Parallel photonic accelerator for decision making using optical spatiotemporal chaos

arXiv: https://arxiv.org/abs/2210.06976 · HTML: https://mlsanity.com/paper/2210.06976/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 50 references that are in the map.
- First posted on arXiv 12 Oct 2022.

## Builds on (most cited first, 10 of 10)

- [Photonics for artificial intelligence and neuromorphic computing](https://mlsanity.com/paper/2011.00111/) (2020)
- [Reinforcement Learning in a large scale photonic Recurrent Neural Network](https://mlsanity.com/paper/1711.05133/) (2017)
- [Scalable Optical Learning Operator](https://mlsanity.com/paper/2012.12404/) (2020)
- [Human action recognition with a large-scale brain-inspired photonic computer](https://mlsanity.com/paper/2004.02545/) (2020)
- [Ultrafast photonic reinforcement learning based on laser chaos](https://mlsanity.com/paper/1704.04379/) (2017)
- [Efficient Decision-Making by Volume-Conserving Physical Object](https://mlsanity.com/paper/1412.6141/) (2014)
- [Scalable photonic reinforcement learning by time-division multiplexing of laser chaos](https://mlsanity.com/paper/1803.09425/) (2018)
- [DeepRMSA: A Deep Reinforcement Learning Framework for Routing, Modulation and Spectrum Assignment in Elastic Optical Networks](https://mlsanity.com/paper/1905.02248/) (2019)
- [Dynamic channel selection in wireless communications via a multi-armed bandit algorithm using laser chaos time series](https://mlsanity.com/paper/1909.03629/) (2019)
- [Controlling chaotic itinerancy in laser dynamics for reinforcement learning](https://mlsanity.com/paper/2205.05987/) (2022)

## Built on by (most cited first, 6 of 6)

- [Conflict-free joint decision by lag and zero-lag synchronization in laser network](https://mlsanity.com/paper/2307.15373/) (2023)
- [Asymmetric leader-laggard cluster synchronization for collective decision-making with laser network](https://mlsanity.com/paper/2312.02537/) (2023)
- [Decentralized multi-agent reinforcement learning algorithm using a cluster-synchronized laser network](https://mlsanity.com/paper/2407.09124/) (2024)
- [Autocorrelation effects in a stochastic-process model for solving two-armed bandit problems](https://mlsanity.com/paper/2603.05559/) (2026)
- [Photonic reservoir computing with complex networks](https://mlsanity.com/paper/2607.23285/) (2026)
- [Massively Parallel Reinforcement Learning with a Chaotic Reconfigurable Clockless Chip](https://mlsanity.com/paper/2609.36347/) (2026)

## Abstract (arXiv)

Photonic accelerators have attracted increasing attention in artificial intelligence applications. The multi-armed bandit problem is a fundamental problem of decision making using reinforcement learning. However, the scalability of photonic decision making has not yet been demonstrated in experiments, owing to technical difficulties in physical realization. We propose a parallel photonic decision-making system for solving large-scale multi-armed bandit problems using optical spatiotemporal chaos. We solve a 512-armed bandit problem online, which is much larger than previous experiments by two orders of magnitude. The scaling property for correct decision making is examined as a function of the number of slot machines, evaluated as an exponent of 0.86. This exponent is smaller than that in previous work, indicating the superiority of the proposed parallel principle. This experimental demonstration facilitates photonic decision making to solve large-scale multi-armed bandit problems for future photonic accelerators.