Entropy Computing, A Paradigm for Optimization in Open Photonic Systems
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909874214928384 |
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| author | Nguyen, Lac Miri, Mohammad-Ali Rupert, R. Joseph Dyk, Wesley Wu, Sam Vrahoretis, Nick Huang, Irwin Begliarbekov, Milan Chancellor, Nicholas Chukwu, Uchenna Mahamuni, Pranav Martinez-Delgado, Cesar Haycraft, David Spear, Carrie Huffman, Joel Russell Sua, Yong Meng Huang, Yu-Ping |
| author_facet | Nguyen, Lac Miri, Mohammad-Ali Rupert, R. Joseph Dyk, Wesley Wu, Sam Vrahoretis, Nick Huang, Irwin Begliarbekov, Milan Chancellor, Nicholas Chukwu, Uchenna Mahamuni, Pranav Martinez-Delgado, Cesar Haycraft, David Spear, Carrie Huffman, Joel Russell Sua, Yong Meng Huang, Yu-Ping |
| contents | Finding better solutions to combinatorial optimization problems could have a large positive impact on many real-world application areas, such as logistics. For this reason, significant efforts have been made to design novel optimisation paradigms. Here we show an early instance of such paradigm in an optical setting, the entropy computing paradigm. Specifically, we experimentally demonstrate the feasibility of entropy computing by building a hybrid photonic-electronic computer that uses optical measurement and feedback to solve non-convex optimization problems. The system functions by using temporal photonic modes to create qudits in order to encode probability amplitudes in the time-frequency degree of freedom of a photon. This scheme, when coupled with with electronic interconnects, allows us to encode an arbitrary Hamiltonian into the system and solve non-convex continuous variables and combinatorial optimization problems. We show that the proposed entropy computing paradigm can act as a scalable and versatile platform for tackling a large range of NP-hard optimization problems. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_04512 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Entropy Computing, A Paradigm for Optimization in Open Photonic Systems Nguyen, Lac Miri, Mohammad-Ali Rupert, R. Joseph Dyk, Wesley Wu, Sam Vrahoretis, Nick Huang, Irwin Begliarbekov, Milan Chancellor, Nicholas Chukwu, Uchenna Mahamuni, Pranav Martinez-Delgado, Cesar Haycraft, David Spear, Carrie Huffman, Joel Russell Sua, Yong Meng Huang, Yu-Ping Quantum Physics Optics Finding better solutions to combinatorial optimization problems could have a large positive impact on many real-world application areas, such as logistics. For this reason, significant efforts have been made to design novel optimisation paradigms. Here we show an early instance of such paradigm in an optical setting, the entropy computing paradigm. Specifically, we experimentally demonstrate the feasibility of entropy computing by building a hybrid photonic-electronic computer that uses optical measurement and feedback to solve non-convex optimization problems. The system functions by using temporal photonic modes to create qudits in order to encode probability amplitudes in the time-frequency degree of freedom of a photon. This scheme, when coupled with with electronic interconnects, allows us to encode an arbitrary Hamiltonian into the system and solve non-convex continuous variables and combinatorial optimization problems. We show that the proposed entropy computing paradigm can act as a scalable and versatile platform for tackling a large range of NP-hard optimization problems. |
| title | Entropy Computing, A Paradigm for Optimization in Open Photonic Systems |
| topic | Quantum Physics Optics |
| url | https://arxiv.org/abs/2407.04512 |