Fully Programmable Spatial Photonic Ising Machine by Focal Plane Division
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866909348023762944 |
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| author | Veraldi, Daniele Pierangeli, Davide Gentilini, Silvia Strinati, Marcello Calvanese Sakellariou, Jason Cummins, James S. Kamaletdinov, Airat Syed, Marvin Wang, Richard Zhipeng Berloff, Natalia G. Karanikolopoulos, Dimitrios Savvidis, Pavlos G. Conti, Claudio |
| author_facet | Veraldi, Daniele Pierangeli, Davide Gentilini, Silvia Strinati, Marcello Calvanese Sakellariou, Jason Cummins, James S. Kamaletdinov, Airat Syed, Marvin Wang, Richard Zhipeng Berloff, Natalia G. Karanikolopoulos, Dimitrios Savvidis, Pavlos G. Conti, Claudio |
| contents | Ising machines are an emerging class of hardware that promises ultrafast and energy-efficient solutions to NP-hard combinatorial optimization problems. Spatial photonic Ising machines (SPIMs) exploit optical computing in free space to accelerate the computation, showcasing parallelism, scalability, and low power consumption. However, current SPIMs can implement only a restricted class of problems. This partial programmability is a critical limitation that hampers their benchmark. Achieving full programmability of the device while preserving its scalability is an open challenge. Here, we report a fully programmable SPIM achieved through a novel operation method based on the division of the focal plane. In our scheme, a general Ising problem is decomposed into a set of Mattis Hamiltonians, whose energies are simultaneously computed optically by measuring the intensity on different regions of the camera sensor. Exploiting this concept, we experimentally demonstrate the computation with high success probability of ground-state solutions of up to 32-spin Ising models on unweighted maximum cut graphs with and without ferromagnetic bias. Simulations of the hardware prove a favorable scaling of the accuracy with the number of spins. Our fully programmable SPIM enables the implementation of many quadratic unconstrained binary optimization problems, further establishing SPIMs as a leading paradigm in non von Neumann hardware. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_10689 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fully Programmable Spatial Photonic Ising Machine by Focal Plane Division Veraldi, Daniele Pierangeli, Davide Gentilini, Silvia Strinati, Marcello Calvanese Sakellariou, Jason Cummins, James S. Kamaletdinov, Airat Syed, Marvin Wang, Richard Zhipeng Berloff, Natalia G. Karanikolopoulos, Dimitrios Savvidis, Pavlos G. Conti, Claudio Optics Disordered Systems and Neural Networks Emerging Technologies Applied Physics Ising machines are an emerging class of hardware that promises ultrafast and energy-efficient solutions to NP-hard combinatorial optimization problems. Spatial photonic Ising machines (SPIMs) exploit optical computing in free space to accelerate the computation, showcasing parallelism, scalability, and low power consumption. However, current SPIMs can implement only a restricted class of problems. This partial programmability is a critical limitation that hampers their benchmark. Achieving full programmability of the device while preserving its scalability is an open challenge. Here, we report a fully programmable SPIM achieved through a novel operation method based on the division of the focal plane. In our scheme, a general Ising problem is decomposed into a set of Mattis Hamiltonians, whose energies are simultaneously computed optically by measuring the intensity on different regions of the camera sensor. Exploiting this concept, we experimentally demonstrate the computation with high success probability of ground-state solutions of up to 32-spin Ising models on unweighted maximum cut graphs with and without ferromagnetic bias. Simulations of the hardware prove a favorable scaling of the accuracy with the number of spins. Our fully programmable SPIM enables the implementation of many quadratic unconstrained binary optimization problems, further establishing SPIMs as a leading paradigm in non von Neumann hardware. |
| title | Fully Programmable Spatial Photonic Ising Machine by Focal Plane Division |
| topic | Optics Disordered Systems and Neural Networks Emerging Technologies Applied Physics |
| url | https://arxiv.org/abs/2410.10689 |