Machine learning with knowledge constraints for design optimization of microring resonators as a quantum light source
Abstract With careful design and integration, microring resonators can serve as a promising foundation for developing compact and scalable sources of non-classical light for quantum information processing. However, the current design flow is hindered by computational challenges and a complex, high-d...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-01-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-84560-4 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841559777966030848 |
|---|---|
| author | Parisa Sadeghli Dizaji Hamidreza Habibiyan |
| author_facet | Parisa Sadeghli Dizaji Hamidreza Habibiyan |
| author_sort | Parisa Sadeghli Dizaji |
| collection | DOAJ |
| description | Abstract With careful design and integration, microring resonators can serve as a promising foundation for developing compact and scalable sources of non-classical light for quantum information processing. However, the current design flow is hindered by computational challenges and a complex, high-dimensional parameter space with interdependent variables. In this work, we present a knowledge-integrated machine learning framework based on Bayesian Optimization for designing squeezed light sources using microring resonators. Our model, after only 5 optimization rounds, identified two optimal structures with distinct cross-sectional areas and radii (65 $$\:\mu\:m$$ and 110 $$\:\mu\:m$$ ), achieving escape efficiencies over 90% and on-chip squeezing levels of 7.48 dB and 9.86 dB, respectively. Our results demonstrate that by adaptively finding the coupling coefficient through BO, the model has identified optimal points in the over-coupled regions with superior performance. This optimization model is developed specifically for single resonators made of silicon nitride. However, its applicability extends beyond this, and it can be used to model structures with auxiliary rings or other materials like silicon carbide. Our approach is expected to streamline the design of other integrated photonic components, including Mach-Zehnder interferometers and directional couplers, for applications in quantum photonic circuits and optical neural networks. |
| format | Article |
| id | doaj-art-f032682b5d1345bc9bfd1e09b96c1e36 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-f032682b5d1345bc9bfd1e09b96c1e362025-01-05T12:15:23ZengNature PortfolioScientific Reports2045-23222025-01-0115111110.1038/s41598-024-84560-4Machine learning with knowledge constraints for design optimization of microring resonators as a quantum light sourceParisa Sadeghli Dizaji0Hamidreza Habibiyan1Departemant of Physics and Energy Engineering, Amirkabir University of TechnologyDepartemant of Physics and Energy Engineering, Amirkabir University of TechnologyAbstract With careful design and integration, microring resonators can serve as a promising foundation for developing compact and scalable sources of non-classical light for quantum information processing. However, the current design flow is hindered by computational challenges and a complex, high-dimensional parameter space with interdependent variables. In this work, we present a knowledge-integrated machine learning framework based on Bayesian Optimization for designing squeezed light sources using microring resonators. Our model, after only 5 optimization rounds, identified two optimal structures with distinct cross-sectional areas and radii (65 $$\:\mu\:m$$ and 110 $$\:\mu\:m$$ ), achieving escape efficiencies over 90% and on-chip squeezing levels of 7.48 dB and 9.86 dB, respectively. Our results demonstrate that by adaptively finding the coupling coefficient through BO, the model has identified optimal points in the over-coupled regions with superior performance. This optimization model is developed specifically for single resonators made of silicon nitride. However, its applicability extends beyond this, and it can be used to model structures with auxiliary rings or other materials like silicon carbide. Our approach is expected to streamline the design of other integrated photonic components, including Mach-Zehnder interferometers and directional couplers, for applications in quantum photonic circuits and optical neural networks.https://doi.org/10.1038/s41598-024-84560-4Quantum light sourcesIntegrated photonicsMicroring resonatorsMachine learningBayesian optimization |
| spellingShingle | Parisa Sadeghli Dizaji Hamidreza Habibiyan Machine learning with knowledge constraints for design optimization of microring resonators as a quantum light source Scientific Reports Quantum light sources Integrated photonics Microring resonators Machine learning Bayesian optimization |
| title | Machine learning with knowledge constraints for design optimization of microring resonators as a quantum light source |
| title_full | Machine learning with knowledge constraints for design optimization of microring resonators as a quantum light source |
| title_fullStr | Machine learning with knowledge constraints for design optimization of microring resonators as a quantum light source |
| title_full_unstemmed | Machine learning with knowledge constraints for design optimization of microring resonators as a quantum light source |
| title_short | Machine learning with knowledge constraints for design optimization of microring resonators as a quantum light source |
| title_sort | machine learning with knowledge constraints for design optimization of microring resonators as a quantum light source |
| topic | Quantum light sources Integrated photonics Microring resonators Machine learning Bayesian optimization |
| url | https://doi.org/10.1038/s41598-024-84560-4 |
| work_keys_str_mv | AT parisasadeghlidizaji machinelearningwithknowledgeconstraintsfordesignoptimizationofmicroringresonatorsasaquantumlightsource AT hamidrezahabibiyan machinelearningwithknowledgeconstraintsfordesignoptimizationofmicroringresonatorsasaquantumlightsource |