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Spin Dynamics, Pulsed Decoupling, and Deep Reinforcement Learning for Quantum Sensing
Spin Dynamics, Pulsed Decoupling, and Deep Reinforcement Learning for Quantum Sensing
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202104831
- ISBN
- 9798293835560
- DDC
- 530
- 저자명
- Oon, Jner Tzern.
- 서명/저자
- Spin Dynamics, Pulsed Decoupling, and Deep Reinforcement Learning for Quantum Sensing
- 발행사항
- [Sl] : University of Maryland, College Park, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 138 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-03, Section: B.
- 주기사항
- Advisor: Walsworth, Ronald.
- 학위논문주기
- Thesis (Ph.D.)--University of Maryland, College Park, 2025.
- 초록/해제
- 요약Quantum sensing faces challenges in moving from proof-of-concept demonstrations to practical technologies, with discrepancies between idealized theoretical frameworks and experimental realities presenting a key obstacle. This dissertation explores these nuances - with a focus on nitrogen-vacancy (NV) centers in diamond - through four projects spanning experiment, theory, simulations, and algorithmic optimization.We characterize Ramsey envelope modulation effects in 15NV diamond magnetometry, showing that magnetic field misalignments produce envelope effects that degrade sensitivity. Next, we study the breakdown of Average Hamiltonian Theory (AHT) in experimental regimes, introduce exact methods to calculate a sensor response to a target signal that are valid beyond AHT, and establish symmetries that guarantee AHT convergence. With Ensemble Cluster Sampling (ECS), we address overfitting in dynamical decoupling by training algorithms on heterogeneous parameter distributions rather than idealized systems. Finally, we present TEMPO, an open-source Python package for accessible pulse sequence simulations.These studies underline the need for continued collaboration between quantum sensing theory and laboratory applications, while maintaining an eye on modern algorithms and software.
- 일반주제명
- Physics
- 일반주제명
- Quantum physics
- 일반주제명
- Nuclear physics
- 일반주제명
- Computational physics
- 키워드
- Quantum sensing
- 키워드
- Spin dynamics
- 기타저자
- University of Maryland, College Park Physics
- 기본자료저록
- Dissertations Abstracts International. 87-03B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798293835560
■035 ▼a(MiAaPQ)AAI32170650
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a530
■1001 ▼aOon, Jner Tzern.▼0(orcid)0000-0002-5716-5653
■24510▼aSpin Dynamics, Pulsed Decoupling, and Deep Reinforcement Learning for Quantum Sensing
■260 ▼a[Sl]▼bUniversity of Maryland, College Park▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a138 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-03, Section: B.
■500 ▼aAdvisor: Walsworth, Ronald.
■5021 ▼aThesis (Ph.D.)--University of Maryland, College Park, 2025.
■520 ▼aQuantum sensing faces challenges in moving from proof-of-concept demonstrations to practical technologies, with discrepancies between idealized theoretical frameworks and experimental realities presenting a key obstacle. This dissertation explores these nuances - with a focus on nitrogen-vacancy (NV) centers in diamond - through four projects spanning experiment, theory, simulations, and algorithmic optimization.We characterize Ramsey envelope modulation effects in 15NV diamond magnetometry, showing that magnetic field misalignments produce envelope effects that degrade sensitivity. Next, we study the breakdown of Average Hamiltonian Theory (AHT) in experimental regimes, introduce exact methods to calculate a sensor response to a target signal that are valid beyond AHT, and establish symmetries that guarantee AHT convergence. With Ensemble Cluster Sampling (ECS), we address overfitting in dynamical decoupling by training algorithms on heterogeneous parameter distributions rather than idealized systems. Finally, we present TEMPO, an open-source Python package for accessible pulse sequence simulations.These studies underline the need for continued collaboration between quantum sensing theory and laboratory applications, while maintaining an eye on modern algorithms and software.
■590 ▼aSchool code: 0117.
■650 4▼aPhysics
■650 4▼aQuantum physics
■650 4▼aNuclear physics
■650 4▼aComputational physics
■653 ▼aDeep reinforcement learning
■653 ▼aNuclear magnetic resonance
■653 ▼aQuantum sensing
■653 ▼aSpin dynamics
■653 ▼aEnsemble Cluster Sampling
■690 ▼a0605
■690 ▼a0599
■690 ▼a0756
■690 ▼a0216
■71020▼aUniversity of Maryland, College Park▼bPhysics.
■7730 ▼tDissertations Abstracts International▼g87-03B.
■790 ▼a0117
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17359079▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
