Frequency‐multiplexed optical reservoir computing using a microcomb
Optical reservoir computing (ORC) promises fast, energy-efficient temporal inference by harnessing the rich transient dynamics of photonic systems. Yet most ORC demonstrations still depend on fiber delay lines or camera-based spatial multiplexing, which caps the clock rate at a few tens of MSa/s and complicates monolithic integration. Here we introduce a frequency-multiplexed ORC whose nodes are the individual modes of a dissipative Kerr-soliton microcomb generated in a high-<i>Q</i> Si<sub>3</sub>N<sub>4</sub> microresonator. The input signal is encoded as a rapid detuning modulation of the pump laser, so the intracavity dynamics of the microcomb provide both the high-dimensional nonlinear mapping and tens of nanoseconds of memory, while output weighting is realized optically with standard microring arrays. Numerical modeling with 60 comb modes provides a normalized mean-square error (NMSE) of 0.015 on the Santa Fe chaotic time-series task at 50 MSa/s and more than a tenfold reduction in symbol-error rate for nonlinear equalization (NLEQ) at 100 MSa/s. A proof-of-concept experiment using 37 measured modes also confirms the concept on the Santa Fe chaotic time-series and NLEQ benchmarks. Because both the microcomb and weighting network are fabricated by a complementary metal-oxide semiconductor (CMOS)-compatible process, the architecture offers a clear path toward compact, energy-efficient photonic processors operating at greater than 1 GSa/s, directly addressing the scalability and speed challenges of nanophotonic ORC.
Authors
- Kaoru Minoshima (ORCID: https://orcid.org/0000-0003-1301-1406)
- Baoqi Shi (ORCID: https://orcid.org/0000-0002-3828-5131)
- Naoya Kuse
- Jonathan Cuevas (ORCID: https://orcid.org/0009-0000-5109-5909)
- Junqiu Liu (ORCID: https://orcid.org/0000-0003-2405-6028)
- Yue Hu (ORCID: https://orcid.org/0009-0000-0275-4090)
Institutions
- University of Science and Technology of China (CN)
- Hefei University of Technology (CN)
- University of Electro-Communications (JP)
- Hefei National Center for Physical Sciences at Nanoscale (CN)
- Beijing Academy of Quantum Information Sciences (CN)
- Tokushima College of Technology (JP)
- Tokushima University (JP)
Publication Details
- Journal
- Nanophotonics
- Published
- 2025-08-28
- DOI
- https://doi.org/10.1515/nanoph-2025-0260
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Japan Science and Technology Corporation
- Cabinet Office, Government of Japan
- Support Center for Advanced Telecommunications Technology Research Foundation
- Murata Science Foundation
- Japan Society for the Promotion of Science