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Excursion in the Quantum Loss Landscape: Learning, Generating, and Simulating in the Quantum World
Excursion in the Quantum Loss Landscape: Learning, Generating, and Simulating in the Quantum World
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20250211152650
- ISBN
- 9798384424093
- DDC
- 530.1
- 저자명
- Rad, Ali.
- 서명/저자
- Excursion in the Quantum Loss Landscape: Learning, Generating, and Simulating in the Quantum World
- 발행사항
- [Sl] : University of Maryland, College Park, 2024
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2024
- 형태사항
- 227 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 86-03, Section: B.
- 주기사항
- Advisor: Hafezi, Mohammad;Gullans, Michael.
- 학위논문주기
- Thesis (Ph.D.)--University of Maryland, College Park, 2024.
- 초록/해제
- 요약Statistical learning is emerging as a new paradigm in science. This has ignited interest within our inherently quantum world in exploring quantum machines for their advantages in learning, generating, and predicting various aspects of our universe by processing both quantum and classical data. In parallel, the pursuit of scalable science through physical simulations using both digital and analog quantum computers is rising on the horizon.In the first part, we investigate how physics can help classical Artificial Intelligence (AI) by studying hybrid classical-quantum algorithms. We focus on quantum generative models and address challenges like barren plateaus during the training of quantum machines. We further examine the generalization capabilities of quantum machine learning models, phase transitions in the over-parameterized regime using random matrix theory, and their effective behavior approximated by Gaussian processes.In the second part, we explore how AI can benefit physics. We demonstrate how classical Machine Learning (ML) models can assist in state recognition in qubit systems within solid-state devices. Additionally, we show how ML-inspired optimization methods can enhance the efficiency of digital quantum simulations with ion-trap setups.Finally, in the third part, we focus on how physics can help physics by using quantum systems to simulate other quantum systems. We propose native fermionic analog quantum systems with fermion-spin systems in silicon to explore non-perturbative phenomena in quantum field theory, offering early applications for lattice gauge theory models.
- 일반주제명
- Quantum physics
- 일반주제명
- Mechanical engineering
- 일반주제명
- Physics
- 일반주제명
- Computer science
- 일반주제명
- Theoretical physics
- 키워드
- Quantum circuits
- 기타저자
- University of Maryland, College Park Physics
- 기본자료저록
- Dissertations Abstracts International. 86-03B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■00520250211152650
■006m o d
■007cr#unu||||||||
■020 ▼a9798384424093
■035 ▼a(MiAaPQ)AAI31486622
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a530.1
■1001 ▼aRad, Ali.
■24510▼aExcursion in the Quantum Loss Landscape: Learning, Generating, and Simulating in the Quantum World
■260 ▼a[Sl]▼bUniversity of Maryland, College Park▼c2024
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2024
■300 ▼a227 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 86-03, Section: B.
■500 ▼aAdvisor: Hafezi, Mohammad;Gullans, Michael.
■5021 ▼aThesis (Ph.D.)--University of Maryland, College Park, 2024.
■520 ▼aStatistical learning is emerging as a new paradigm in science. This has ignited interest within our inherently quantum world in exploring quantum machines for their advantages in learning, generating, and predicting various aspects of our universe by processing both quantum and classical data. In parallel, the pursuit of scalable science through physical simulations using both digital and analog quantum computers is rising on the horizon.In the first part, we investigate how physics can help classical Artificial Intelligence (AI) by studying hybrid classical-quantum algorithms. We focus on quantum generative models and address challenges like barren plateaus during the training of quantum machines. We further examine the generalization capabilities of quantum machine learning models, phase transitions in the over-parameterized regime using random matrix theory, and their effective behavior approximated by Gaussian processes.In the second part, we explore how AI can benefit physics. We demonstrate how classical Machine Learning (ML) models can assist in state recognition in qubit systems within solid-state devices. Additionally, we show how ML-inspired optimization methods can enhance the efficiency of digital quantum simulations with ion-trap setups.Finally, in the third part, we focus on how physics can help physics by using quantum systems to simulate other quantum systems. We propose native fermionic analog quantum systems with fermion-spin systems in silicon to explore non-perturbative phenomena in quantum field theory, offering early applications for lattice gauge theory models.
■590 ▼aSchool code: 0117.
■650 4▼aQuantum physics
■650 4▼aMechanical engineering
■650 4▼aPhysics
■650 4▼aComputer science
■650 4▼aTheoretical physics
■653 ▼aAnalog quantum simulation
■653 ▼aQuantum circuits
■653 ▼aQuantum machine learning
■653 ▼aQuantum simulations
■653 ▼aStatistical learning
■690 ▼a0599
■690 ▼a0548
■690 ▼a0605
■690 ▼a0984
■690 ▼a0753
■71020▼aUniversity of Maryland, College Park▼bPhysics.
■7730 ▼tDissertations Abstracts International▼g86-03B.
■790 ▼a0117
■791 ▼aPh.D.
■792 ▼a2024
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17163297▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
