Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
184 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Asymmetric leader-laggard cluster synchronization for collective decision-making with laser network (2312.02537v1)

Published 5 Dec 2023 in physics.optics, cs.LG, and nlin.CD

Abstract: Photonic accelerators have recently attracted soaring interest, harnessing the ultimate nature of light for information processing. Collective decision-making with a laser network, employing the chaotic and synchronous dynamics of optically interconnected lasers to address the competitive multi-armed bandit (CMAB) problem, is a highly compelling approach due to its scalability and experimental feasibility. We investigated essential network structures for collective decision-making through quantitative stability analysis. Moreover, we demonstrated the asymmetric preferences of players in the CMAB problem, extending its functionality to more practical applications. Our study highlights the capability and significance of machine learning built upon chaotic lasers and photonic devices.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (36)
  1. K. Kitayama, M. Notomi, M. Naruse, K. Inoue, S. Kawakami, and A. Uchida, “Novel frontier of photonics for data processing—photonic accelerator,” APL Photonics 4, 090901 (2019).
  2. J. Hardy and J. Shamir, “Optics inspired logic architecture,” Optics Express 15, 150–165 (2007).
  3. L. Larger, M. C. Soriano, D. Brunner, L. Appeltant, J. M. Gutierrez, L. Pesquera, C. R. Mirasso, and I. Fischer, “Photonic information processing beyond turing: an optoelectronic implementation of reservoir computing,” Optics Express 20, 3241–3249 (2012).
  4. D. Brunner, M. C. Soriano, C. R. Mirasso, and I. Fischer, “Parallel photonic information processing at gigabyte per second data rates using transient states,” Nature Communications 4, 1–7 (2013).
  5. Y. Shen, N. C. Harris, S. Skirlo, M. Prabhu, T. Baehr-Jones, M. Hochberg, X. Sun, S. Zhao, H. Larochelle, D. Englund et al., “Deep learning with coherent nanophotonic circuits,” Nature Photonics 11, 441–446 (2017).
  6. A. N. Tait, T. F. De Lima, E. Zhou, A. X. Wu, M. A. Nahmias, B. J. Shastri, and P. R. Prucnal, “Neuromorphic photonic networks using silicon photonic weight banks,” Scientific Reports 7, 1–10 (2017).
  7. J. Bueno, S. Maktoobi, L. Froehly, I. Fischer, M. Jacquot, L. Larger, and D. Brunner, “Reinforcement learning in a large-scale photonic recurrent neural network,” Optica 5, 756–760 (2018).
  8. T. Inagaki, Y. Haribara, K. Igarashi, T. Sonobe, S. Tamate, T. Honjo, A. Marandi, P. L. McMahon, T. Umeki, K. Enbutsu, O. Tadanaga, H. Takenouchi, K. Aihara, K. ichi Kawarabayashi, K. Inoue, S. Utsunomiya, and H. Takesue, “A coherent ising machine for 2000-node optimization problems,” Science 354, 603–606 (2016).
  9. J.-H. Han, F. Boeuf, J. Fujikata, S. Takahashi, S. Takagi, and M. Takenaka, “Efficient low-loss ingaasp/si hybrid mos optical modulator,” Nature Photonics 11, 486–490 (2017).
  10. B. Marr, B. Degnan, P. Hasler, and D. Anderson, “Scaling energy per operation via an asynchronous pipeline,” IEEE Transactions on Very Large Scale Integration (VLSI) Systems 21, 147–151 (2012).
  11. D. Isele, R. Rahimi, A. Cosgun, K. Subramanian, and K. Fujimura, “Navigating occluded intersections with autonomous vehicles using deep reinforcement learning,” in 2018 IEEE international conference on robotics and automation (ICRA), (IEEE, 2018), pp. 2034–2039.
  12. D. Agarwal, B.-C. Chen, and P. Elango, “Explore/exploit schemes for web content optimization,” in 2009 Ninth IEEE International Conference on Data Mining, (IEEE, 2009), pp. 1–10.
  13. S. Wang, H. Liu, P. H. Gomes, and B. Krishnamachari, “Deep reinforcement learning for dynamic multichannel access in wireless networks,” IEEE Transactions on Cognitive Communications and Networking 4, 257–265 (2018).
  14. H. Robbins, “Some aspects of the sequential design of experiments,” Bulletin of the American Mathematical Society 58, 527–536 (1952).
  15. L. Lai, H. El Gamal, H. Jiang, and H. V. Poor, “Cognitive medium access: Exploration, exploitation, and competition,” IEEE Transactions on Mobile Computing 10, 239–253 (2010).
  16. M. Naruse, W. Nomura, M. Aono, M. Ohtsu, Y. Sonnefraud, A. Drezet, S. Huant, and S.-J. Kim, “Decision making based on optical excitation transfer via near-field interactions between quantum dots,” Journal of Applied Physics 116, 1–8 (2014).
  17. M. Naruse, M. Berthel, A. Drezet, S. Huant, M. Aono, H. Hori, and S.-J. Kim, “Single-photon decision maker,” Scientific Reports 5, 1–9 (2015).
  18. M. Naruse, Y. Terashima, A. Uchida, and S.-J. Kim, “Ultrafast photonic reinforcement learning based on laser chaos,” Scientific Reports 7, 1–10 (2017).
  19. R. Homma, S. Kochi, T. Niiyama, T. Mihana, Y. Mitsui, K. Kanno, A. Uchida, M. Naruse, and S. Sunada, “On-chip photonic decision maker using spontaneous mode switching in a ring laser,” Scientific Reports 9, 1–9 (2019).
  20. T. Mihana, Y. Mitsui, M. Takabayashi, K. Kanno, S. Sunada, M. Naruse, and A. Uchida, “Decision making for the multi-armed bandit problem using lag synchronization of chaos in mutually coupled semiconductor lasers,” Optics Express 27, 26989–27008 (2019).
  21. R. Iwami, T. Mihana, K. Kanno, S. Sunada, M. Naruse, and A. Uchida, “Controlling chaotic itinerancy in laser dynamics for reinforcement learning,” Science Advances 8, eabn8325 (2022).
  22. K. Morijiri, K. Takehana, T. Mihana, K. Kanno, M. Naruse, and A. Uchida, “Parallel photonic accelerator for decision making using optical spatiotemporal chaos,” Optica 10, 339–348 (2023).
  23. H. Ito, T. Mihana, R. Horisaki, and M. Naruse, “Conflict-free joint decision by lag and zero-lag synchronization in laser network,” arXiv:2307.15373 (2023).
  24. T. Heil, I. Fischer, W. Elsßsser, J. Mulet, and C. R. Mirasso, “Chaos synchronization and spontaneous symmetry-breaking in symmetrically delay-coupled semiconductor lasers,” Physical Review Letters 86, 795–798 (2001).
  25. K. Kanno, T. Hida, A. Uchida, and M. Bunsen, “Spontaneous exchange of leader-laggard relationship in mutually coupled synchronized semiconductor lasers,” Physical Review E 95, 052212 (2017).
  26. T. Sano, “Antimode dynamics and chaotic itinerancy in the coherence collapse of semiconductor lasers with optical feedback,” Physical Review A 50, 2719 (1994).
  27. T. Mihana, K. Fujii, K. Kanno, M. Naruse, and A. Uchida, “Laser network decision making by lag synchronization of chaos in a ring configuration,” Optics Express 28, 40112–40130 (2020).
  28. I. Fischer, R. Vicente, J. M. Buldu, M. Peil, C. R. Mirasso, M. C. Torrent, and J. Garcia-Ojalvo, “Zero-lag long-range synchronization via dynamical relaying,” Physical Review Letters 97, 123902 (2006).
  29. M. Nixon, M. Friedman, E. Ronen, A. A. Friesem, N. Davidson, and I. Kanter, “Synchronized cluster formation in coupled laser networks,” Physical Review Letters 106, 223901 (2011).
  30. M. Nixon, M. Fridman, E. Ronen, A. A. Friesem, N. Davidson, and I. Kanter, “Controlling synchronization in large laser networks,” Physical Review Letters 108, 214101 (2012).
  31. J. Ohtsubo, R. Ozawa, and M. Nanbu, “Synchrony of small nonlinear networks in chaotic semiconductor lasers,” Japanese Journal Applied Physical p. 072702 (2015).
  32. T. Dahms, J. Lehnert, and E. Schöll, “Cluster and group synchronization in delay-coupled networks,” Physical Review E 86, 016202 (2012).
  33. L. M. Pecora, F. Sorrentino, A. M. Hagerstrom, T. E. Murphy, and R. Roy, “Cluster synchronization and isolated desynchronization in complex networks with symmetries,” Nature Communications 5, 1–8 (2014).
  34. R. Lang and K. Kobayashi, “External optical feedback effects on semiconductor injection laser properties,” IEEE Journal of Quantum Electronics 16, 347–355 (1980).
  35. L. M. Pecora and T. L. Carroll, “Synchronization in chaotic systems,” Physical review letters 64, 821 (1990).
  36. L. M. Pecora and T. L. Carroll, “Driving systems with chaotic signals,” Physical review A 44, 2374 (1991).
Citations (1)
View on Semantic Scholar

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now