東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Scalable Quantum Compilation Approac...

Saravanan, Vedika.

FindBook

Google Book

Amazon

博客來

Scalable Quantum Compilation Approaches for Reliable Quantum Computing.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Scalable Quantum Compilation Approaches for Reliable Quantum Computing./
作者:	Saravanan, Vedika.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	169 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
Contained By:	Dissertations Abstracts International85-01B.
標題:	Electrical engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30523640
ISBN:	9798379917081

Scalable Quantum Compilation Approaches for Reliable Quantum Computing.
Saravanan, Vedika.

Scalable Quantum Compilation Approaches for Reliable Quantum Computing. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 169 p.

Source: Dissertations Abstracts International, Volume: 85-01, Section: B.

Thesis (Ph.D.)--The City College of New York, 2023.

This item must not be sold to any third party vendors.

Quantum computing has the potential to revolutionize various industries, from pharmaceuticals to finance and transportation. Unlike classical computers, quantum computers are based on the principles of quantum mechanics and can perform specific calculations exponentially faster than classical computers. However, quantum computing is still in its early stages of development. It faces several challenges, including the high rate of errors and the limited number of qubits in quantum hardware. Therefore, more efficient and scalable methods are needed to translate quantum algorithms into executable forms on a quantum computer. To overcome these challenges, several research efforts are underway to develop scalable noise-aware quantum compilation approaches that can enhance the reliability of quantum computers using minimal hardware resources and time.The first key contribution of the research presented in this thesis is the development of predictive methodologies to improve the reliability of quantum circuits. They include probabilistic and Machine Learning (ML) reliability models applied to different quantum circuit design abstractions. They are designed to capture the impact of different quantum hardware errors on the output fidelity of quantum circuits. Our proposed models make real-time predictions of the output state fidelity of quantum circuits.The second key contribution of this research is the development of compilation and error mitigation approaches that are driven by our proposed predictive techniques including gate rescheduling and idling qubits error mitigation. They are designed to suppress different types of errors. The research results demonstrate that analyzing the quantum circuit structure can play a vital role in mitigating errors in quantum circuits. The proposed approaches are validated on various quantum algorithms executed on real world IBM quantum machines. The results show that our approaches can better predict and improve the output fidelity of the quantum circuits in the presence of various errors in the quantum hardware.

ISBN: 9798379917081Subjects--Topical Terms:

649834
Electrical engineering.
Subjects--Index Terms:

Machine Learning

Scalable Quantum Compilation Approaches for Reliable Quantum Computing.
LDR:03281nmm a2200385 4500 001 2393651
005 20240414211503.5
006 m o d
007 cr#unu||||||||
008 251215s2023 ||||||||||||||||| ||eng d
020 $a 9798379917081
035 $a (MiAaPQ)AAI30523640
035 $a AAI30523640
040 $a MiAaPQ $c MiAaPQ
100 1 $a Saravanan, Vedika. $3 3763122
245 1 0 $a Scalable Quantum Compilation Approaches for Reliable Quantum Computing.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 169 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-01, Section: B.
500 $a Advisor: Saeed, Samah M.
502 $a Thesis (Ph.D.)--The City College of New York, 2023.
506 $a This item must not be sold to any third party vendors.
520 $a Quantum computing has the potential to revolutionize various industries, from pharmaceuticals to finance and transportation. Unlike classical computers, quantum computers are based on the principles of quantum mechanics and can perform specific calculations exponentially faster than classical computers. However, quantum computing is still in its early stages of development. It faces several challenges, including the high rate of errors and the limited number of qubits in quantum hardware. Therefore, more efficient and scalable methods are needed to translate quantum algorithms into executable forms on a quantum computer. To overcome these challenges, several research efforts are underway to develop scalable noise-aware quantum compilation approaches that can enhance the reliability of quantum computers using minimal hardware resources and time.The first key contribution of the research presented in this thesis is the development of predictive methodologies to improve the reliability of quantum circuits. They include probabilistic and Machine Learning (ML) reliability models applied to different quantum circuit design abstractions. They are designed to capture the impact of different quantum hardware errors on the output fidelity of quantum circuits. Our proposed models make real-time predictions of the output state fidelity of quantum circuits.The second key contribution of this research is the development of compilation and error mitigation approaches that are driven by our proposed predictive techniques including gate rescheduling and idling qubits error mitigation. They are designed to suppress different types of errors. The research results demonstrate that analyzing the quantum circuit structure can play a vital role in mitigating errors in quantum circuits. The proposed approaches are validated on various quantum algorithms executed on real world IBM quantum machines. The results show that our approaches can better predict and improve the output fidelity of the quantum circuits in the presence of various errors in the quantum hardware.
590 $a School code: 1606.
650 4 $a Electrical engineering. $3 649834
650 4 $a Physics. $3 516296
650 4 $a Quantum physics. $3 726746
653 $a Machine Learning
653 $a Quantum circuits
653 $a Quantum mechanics
653 $a Quantum compilation approaches
690 $a 0544
690 $a 0599
690 $a 0605
710 2 $a The City College of New York. $b Electrical Engineering. $3 2095374
773 0 $t Dissertations Abstracts International $g 85-01B.
790 $a 1606
791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30523640