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Photonic Reservoir Computing.

Kumar, Prajnesh Vijay.

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Photonic Reservoir Computing.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Photonic Reservoir Computing./
作者:	Kumar, Prajnesh Vijay.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	164 p.
附註:	Source: Dissertations Abstracts International, Volume: 84-11, Section: B.
Contained By:	Dissertations Abstracts International84-11B.
標題:	Physics. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30487066
ISBN:	9798379565701

Photonic Reservoir Computing.
Kumar, Prajnesh Vijay.

Photonic Reservoir Computing. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 164 p.

Source: Dissertations Abstracts International, Volume: 84-11, Section: B.

Thesis (Ph.D.)--Stevens Institute of Technology, 2023.

Photonic reservoir computing (PRC) has emerged as a pioneering paradigm for next generation computing, offering remarkable speed, energy efficiency, and scalability compared to traditional electronic-based systems. This dissertation presents an in-depth investigation of time delay reservoir computing techniques that leverage the inherent properties of photonics to create a temporal reservoir by introducing delays, either digitally or through fiber components, and exploiting the nonlinearity of electrooptic modulators. The primary goal is to design and implement an experimental setup and subsequently evaluate the performance and robustness of these time delay reservoir computing systems across a range of tasks, including function fitting, prediction, and classification.The thesis first establishes a strong foundation by offering a detailed review of the theoretical underpinnings of time delay reservoir computing, emphasizing the advantages conferred by the photonic implementation. The design and implementation of the experimental setup are then thoroughly examined, including the selection and configuration of photonic components, delay line arrangement, and electronic elements.Subsequently, the thesis investigates the application of the developed time delay reservoir computing systems in multiple tasks. These tasks encompasses1. Function fitting, where the goal is to estimate unknown functions from given input-output samples.2. Prediction, which involves forecasting future values of a time series based on historical data.3. Classification, wherein input patterns are categorized according to their features.The performance of the time delay reservoir computing systems is assessed in terms of accuracy.In subsequent sections, the thesis discusses topics beyond reservoir computing, including Quantum Random Walk (QRW). It lays out the theoretical foundations for optical circuits and investigates the use of QRW, providing simulation results. This chapter also presents the development and implementation of a quantum hash function using optical circuit and simulated using a Python script. The process of generating 256-bit hash values by incorporating variable phase shifts and encoding data into the phase space of the quantum random walk is discussed. Furthermore, the procedure to create a reference hash value, identify potential collisions, and calculate the Hamming distance for a set of 63-bit random binary numbers is outlined. The key benchmark criteria to evaluate the performance of the hash function are presented.In conclusion, this thesis presents a comprehensive study of time delay reservoir computing techniques, leading to the design, implementation, and evaluation of experimental setups for diverse tasks such as function fitting, prediction, and classification. The results highlight the remarkable performance, robustness, and adaptability of this photonic computing approach, paving the way for future developments in the realm of next-generation computing systems.

ISBN: 9798379565701Subjects--Topical Terms:

516296
Physics.
Subjects--Index Terms:

Machine learning

Photonic Reservoir Computing.
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520 $a Photonic reservoir computing (PRC) has emerged as a pioneering paradigm for next generation computing, offering remarkable speed, energy efficiency, and scalability compared to traditional electronic-based systems. This dissertation presents an in-depth investigation of time delay reservoir computing techniques that leverage the inherent properties of photonics to create a temporal reservoir by introducing delays, either digitally or through fiber components, and exploiting the nonlinearity of electrooptic modulators. The primary goal is to design and implement an experimental setup and subsequently evaluate the performance and robustness of these time delay reservoir computing systems across a range of tasks, including function fitting, prediction, and classification.The thesis first establishes a strong foundation by offering a detailed review of the theoretical underpinnings of time delay reservoir computing, emphasizing the advantages conferred by the photonic implementation. The design and implementation of the experimental setup are then thoroughly examined, including the selection and configuration of photonic components, delay line arrangement, and electronic elements.Subsequently, the thesis investigates the application of the developed time delay reservoir computing systems in multiple tasks. These tasks encompasses1. Function fitting, where the goal is to estimate unknown functions from given input-output samples.2. Prediction, which involves forecasting future values of a time series based on historical data.3. Classification, wherein input patterns are categorized according to their features.The performance of the time delay reservoir computing systems is assessed in terms of accuracy.In subsequent sections, the thesis discusses topics beyond reservoir computing, including Quantum Random Walk (QRW). It lays out the theoretical foundations for optical circuits and investigates the use of QRW, providing simulation results. This chapter also presents the development and implementation of a quantum hash function using optical circuit and simulated using a Python script. The process of generating 256-bit hash values by incorporating variable phase shifts and encoding data into the phase space of the quantum random walk is discussed. Furthermore, the procedure to create a reference hash value, identify potential collisions, and calculate the Hamming distance for a set of 63-bit random binary numbers is outlined. The key benchmark criteria to evaluate the performance of the hash function are presented.In conclusion, this thesis presents a comprehensive study of time delay reservoir computing techniques, leading to the design, implementation, and evaluation of experimental setups for diverse tasks such as function fitting, prediction, and classification. The results highlight the remarkable performance, robustness, and adaptability of this photonic computing approach, paving the way for future developments in the realm of next-generation computing systems.
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