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Abstract
Complex-valued neural networks have many advantages over their real-valued counterparts. Conventional digital electronic computing platforms are incapable of executing truly complex-valued representations and operations. In contrast, optical computing platforms that encode information in both phase and magnitude can execute complex arithmetic by optical interference, offering significantly enhanced computational speed and energy efficiency. However, to date, most demonstrations of optical neural networks still only utilize conventional real-valued frameworks that are designed for digital computers, forfeiting many of the advantages of optical computing such as efficient complex-valued operations. In this article, we highlight an optical neural chip (ONC) that implements truly complex-valued neural networks. We benchmark the performance of our complex-valued ONC in four settings: simple Boolean tasks, species classification of an Iris dataset, classifying nonlinear datasets (Circle and Spiral), and handwriting recognition. Strong learning capabilities (i.e., high accuracy, fast convergence and the capability to construct nonlinear decision boundaries) are achieved by our complex-valued ONC compared to its real-valued counterpart.
Most demonstrations of optical neural networks for computing have been so far limited to real-valued frameworks. Here, the authors implement complex-valued operations in an optical neural chip that integrates input preparation, weight multiplication and output generation within a single device.
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1 Nanyang Technological University, Quantum Science and Engineering Centre (QSec), Singapore, Singapore (GRID:grid.59025.3b) (ISNI:0000 0001 2224 0361)
2 Nanyang Technological University, Complexity Institute and School of Physical and Mathematical Sciences, Singapore, Singapore (GRID:grid.59025.3b) (ISNI:0000 0001 2224 0361); National University of Singapore, Centre for Quantum Technologies, Singapore, Singapore (GRID:grid.4280.e) (ISNI:0000 0001 2180 6431)
3 National University of Singapore, Centre for Quantum Technologies, Singapore, Singapore (GRID:grid.4280.e) (ISNI:0000 0001 2180 6431)
4 A*STAR (Agency for Science, Technology and Research), Institute of Microelectronics, Singapore, Singapore (GRID:grid.185448.4) (ISNI:0000 0004 0637 0221)
5 University of Bristol, Centre for Quantum Photonics, H. H. Wills Physics Laboratory and Department of Electrical and Electronic Engineering, Bristol, UK (GRID:grid.5337.2) (ISNI:0000 0004 1936 7603)
6 Nanyang Technological University, Quantum Science and Engineering Centre (QSec), Singapore, Singapore (GRID:grid.59025.3b) (ISNI:0000 0001 2224 0361); Nanyang Technological University, School of Mechanical & Aerospace Engineering, Singapore, Singapore (GRID:grid.59025.3b) (ISNI:0000 0001 2224 0361)
7 Southern University of Science and Technology, Institute for Quantum Science and Engineering, Department of Physics, Shenzhen, China (GRID:grid.263817.9); Southern University of Science and Technology, Shenzhen Key Laboratory of Quantum Science and Engineering, Shenzhen, China (GRID:grid.263817.9)
8 Advanced Micro Foundry, Singapore, Singapore (GRID:grid.59025.3b)
9 Nanyang Technological University, Quantum Science and Engineering Centre (QSec), Singapore, Singapore (GRID:grid.59025.3b) (ISNI:0000 0001 2224 0361); National University of Singapore, Centre for Quantum Technologies, Singapore, Singapore (GRID:grid.4280.e) (ISNI:0000 0001 2180 6431); National Institute of Education, Singapore, Singapore (GRID:grid.59025.3b) (ISNI:0000 0001 2224 0361)