國立虎尾科技大學 |

Photonic Reservoir Computing.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Photonic Reservoir Computing./
作者:	Kumar, Prajnesh Vijay.
面頁冊數:	1 online resource (164 pages)
附註:	Source: Dissertations Abstracts International, Volume: 84-11, Section: B.
Contained By:	Dissertations Abstracts International84-11B.
標題:	Physics. -
電子資源:	click for full text (PQDT)
ISBN:	9798379565701

Photonic Reservoir Computing.
Kumar, Prajnesh Vijay.

Photonic Reservoir Computing. - 1 online resource (164 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798379565701Subjects--Topical Terms:

564049
Physics.
Subjects--Index Terms:

Machine learningIndex Terms--Genre/Form:

554714
Electronic books.

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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