Accelerating Photonic Integrated Circuit Design: Traditional, ML and Quantum Methods
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arXiv
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| Format: | Preprint |
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2025
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| author | Oquendo, Alessandro Daniele Genuardi Nadir, Ali Jonuzi, Tigers Patra, Siddhartha Sinha, Nilotpal Kanti Orús, Román Mugel, Sam |
| author_facet | Oquendo, Alessandro Daniele Genuardi Nadir, Ali Jonuzi, Tigers Patra, Siddhartha Sinha, Nilotpal Kanti Orús, Román Mugel, Sam |
| contents | Photonic Integrated Circuits (PICs) provide superior speed, bandwidth, and energy efficiency, making them ideal for communication, sensing, and quantum computing applications. Despite their potential, PIC design workflows and integration lag behind those in electronics, calling for groundbreaking advancements. This review outlines the state of PIC design, comparing traditional simulation methods with machine learning approaches that enhance scalability and efficiency. It also explores the promise of quantum algorithms and quantum-inspired methods to address design challenges. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_18435 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Accelerating Photonic Integrated Circuit Design: Traditional, ML and Quantum Methods Oquendo, Alessandro Daniele Genuardi Nadir, Ali Jonuzi, Tigers Patra, Siddhartha Sinha, Nilotpal Kanti Orús, Román Mugel, Sam Quantum Physics Applied Physics Photonic Integrated Circuits (PICs) provide superior speed, bandwidth, and energy efficiency, making them ideal for communication, sensing, and quantum computing applications. Despite their potential, PIC design workflows and integration lag behind those in electronics, calling for groundbreaking advancements. This review outlines the state of PIC design, comparing traditional simulation methods with machine learning approaches that enhance scalability and efficiency. It also explores the promise of quantum algorithms and quantum-inspired methods to address design challenges. |
| title | Accelerating Photonic Integrated Circuit Design: Traditional, ML and Quantum Methods |
| topic | Quantum Physics Applied Physics |
| url | https://arxiv.org/abs/2506.18435 |