Large-scale programmable silicon photonics for quantum and classical machine learning
Photonic technologies provide many unique physical advantages including ultra-high bandwidths, energy-efficient operations, and low coupling to environmental noise. Furthermore, recent advances in foundry-based manufacturing platforms have enabled the emerging field of integrated systems photonics....
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| Format: | Thesis |
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Massachusetts Institute of Technology
2023
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| Online Access: | https://hdl.handle.net/1721.1/150215 |
| _version_ | 1826206146892922880 |
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| author | Prabhu, Mihika |
| author2 | Englund, Dirk R. |
| author_facet | Englund, Dirk R. Prabhu, Mihika |
| author_sort | Prabhu, Mihika |
| collection | MIT |
| description | Photonic technologies provide many unique physical advantages including ultra-high bandwidths, energy-efficient operations, and low coupling to environmental noise. Furthermore, recent advances in foundry-based manufacturing platforms have enabled the emerging field of integrated systems photonics. In contrast to their bulk optics counterparts, these systems can co-integrate dense ensembles of active photonic and electronic components on a single wafer with high phase stability and small device footprints. Initial demonstrations of each element in the integrated photonics stack—sources, processors, and detectors—motivate the development of wafer-scale photonic integrated circuit implementations, which are poised to form a key building block for fundamental advancements in computing, communications, and sensing.
The first part of this thesis will discuss the development and early system-level demonstrations of linear programmable nanophotonic processors in the silicon-on-insulator platform for applications in quantum and classical machine learning and information processing. Using our developed processor architecture, we then present a nanophotonic Ising sampler for noise-assisted combinatorial optimization. Subsequently, we present a novel, foundry-compatible platform for integrating telecommunication-wavelength artificial atom quantum emitters directly in silicon photonic circuits. Finally, we report a capacity analysis of a structured interferometric receiver implemented with a silicon photonic processor for detection of optical signals in photon-sparse communication links. |
| first_indexed | 2024-09-23T13:24:50Z |
| format | Thesis |
| id | mit-1721.1/150215 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T13:24:50Z |
| publishDate | 2023 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1502152023-04-01T03:28:23Z Large-scale programmable silicon photonics for quantum and classical machine learning Prabhu, Mihika Englund, Dirk R. Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Photonic technologies provide many unique physical advantages including ultra-high bandwidths, energy-efficient operations, and low coupling to environmental noise. Furthermore, recent advances in foundry-based manufacturing platforms have enabled the emerging field of integrated systems photonics. In contrast to their bulk optics counterparts, these systems can co-integrate dense ensembles of active photonic and electronic components on a single wafer with high phase stability and small device footprints. Initial demonstrations of each element in the integrated photonics stack—sources, processors, and detectors—motivate the development of wafer-scale photonic integrated circuit implementations, which are poised to form a key building block for fundamental advancements in computing, communications, and sensing. The first part of this thesis will discuss the development and early system-level demonstrations of linear programmable nanophotonic processors in the silicon-on-insulator platform for applications in quantum and classical machine learning and information processing. Using our developed processor architecture, we then present a nanophotonic Ising sampler for noise-assisted combinatorial optimization. Subsequently, we present a novel, foundry-compatible platform for integrating telecommunication-wavelength artificial atom quantum emitters directly in silicon photonic circuits. Finally, we report a capacity analysis of a structured interferometric receiver implemented with a silicon photonic processor for detection of optical signals in photon-sparse communication links. Ph.D. 2023-03-31T14:40:09Z 2023-03-31T14:40:09Z 2023-02 2023-02-28T14:39:26.203Z Thesis https://hdl.handle.net/1721.1/150215 In Copyright - Educational Use Permitted Copyright MIT http://rightsstatements.org/page/InC-EDU/1.0/ application/pdf Massachusetts Institute of Technology |
| spellingShingle | Prabhu, Mihika Large-scale programmable silicon photonics for quantum and classical machine learning |
| title | Large-scale programmable silicon photonics for quantum and classical machine learning |
| title_full | Large-scale programmable silicon photonics for quantum and classical machine learning |
| title_fullStr | Large-scale programmable silicon photonics for quantum and classical machine learning |
| title_full_unstemmed | Large-scale programmable silicon photonics for quantum and classical machine learning |
| title_short | Large-scale programmable silicon photonics for quantum and classical machine learning |
| title_sort | large scale programmable silicon photonics for quantum and classical machine learning |
| url | https://hdl.handle.net/1721.1/150215 |
| work_keys_str_mv | AT prabhumihika largescaleprogrammablesiliconphotonicsforquantumandclassicalmachinelearning |