A monolithically integrated optical Ising machine
The growing demand for enhanced computational power and energy efficiency has driven the development of optical Ising machines for solving combinatorial optimization problems. However, existing implementations face challenges in integration density and energy efficiency. Here, we propose a monolithically integrated four-spin Ising machine based on optoelectronic coupled oscillators. This system integrates a custom-designed Mach-Zehnder interferometer (MZI) symmetric matrix with a high-efficiency optical-electrical coupled (OEC) nonlinear unit. The OEC unit has an ultra-compact 0.01 mm² footprint and achieves a power efficiency of 4 mW per unit, ensuring scalability. The reconfigurable real-valued coupling matrix achieves a mean fidelity of 0.986. The spin evolution time is measured as 150 ns, with a 1.71 ns round-trip time confirmed through bandwidth measurements. The system successfully finds ground states for various four-spin Ising problems, demonstrating its effectiveness. This work represents a significant step toward monolithic integration of all-optical physical annealing systems, minimizing footprint, power consumption, and convergence time.
Authors
- Bo Wu (ORCID: https://orcid.org/0000-0003-4710-3923)
- Shiji Zhang (ORCID: https://orcid.org/0009-0000-9380-5542)
- Dongmei Huang (ORCID: https://orcid.org/0000-0003-0180-9971)
- Hailong Zhou (ORCID: https://orcid.org/0000-0002-1878-4291)
- Jianji Dong (ORCID: https://orcid.org/0000-0002-1852-8650)
- Zhichao Ruan (ORCID: https://orcid.org/0000-0001-8311-6970)
- Ming Li (ORCID: https://orcid.org/0000-0002-0145-486X)
- Xinliang Zhang (ORCID: https://orcid.org/0000-0001-8513-3328)
- Wenkai Zhang (ORCID: https://orcid.org/0000-0002-8903-2708)
Institutions
- Hong Kong Polytechnic University (HK)
- Wuhan National Laboratory for Optoelectronics (CN)
- Institute of Semiconductors (CN)
- Huazhong University of Science and Technology (CN)
- Zhejiang University (CN)
Publication Details
- Journal
- Nature Communications
- Published
- 2025-05-08
- DOI
- https://doi.org/10.1038/s41467-025-59537-0
- Citations
- 17
- Primary Topic
- Neural Networks and Reservoir Computing
- Type
- article
- Field-Weighted Citation Impact
- 42.18
Funders
- National Natural Science Foundation of China