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Advancing Applications of Quantum Computers in Quantum Simulation, Optimization, Learning, and Topological Data Analysis
Advancing Applications of Quantum Computers in Quantum Simulation, Optimization, Learning, and Topological Data Analysis
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202104751
- ISBN
- 9798290651286
- DDC
- 004
- 저자명
- King, Robbie.
- 서명/저자
- Advancing Applications of Quantum Computers in Quantum Simulation, Optimization, Learning, and Topological Data Analysis
- 발행사항
- [Sl] : California Institute of Technology, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 240 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-01, Section: B.
- 주기사항
- Advisor: Schulman, Leonard;Vidick, Thomas.
- 학위논문주기
- Thesis (Ph.D.)--California Institute of Technology, 2025.
- 초록/해제
- 요약This thesis investigates novel directions for harnessing the potential of quantum computers in future applications. It is structured into three sections.Quantum Simulation.We address two key questions: what systems exhibit quantum advantage in predicting ground state properties, and how can we reduce the cost of quantum simulations? For the former, we find that strongly interacting fermionic systems have promising characteristics for quantum advantage. For the latter, we develop an improved method for compiling block encodings using sum-of-squares optimization.Learning with EntangledMeasurements. We explore the benefits of leveraging entangled measurements on quantum states stored in quantum memory. These learning algorithms can be applied to the readout stage of quantum simulations, or to learn from quantum data from nature.Topological Data Analysis.Using complexity-theoretic insights, we demonstrate that certain problems in topological data analysis possess a quantum mechanical structure, suggesting opportunities for quantum algorithms in this area.
- 일반주제명
- Quantum computing
- 일반주제명
- Tomography
- 일반주제명
- Computers
- 일반주제명
- Physics
- 일반주제명
- Phase transitions
- 일반주제명
- Chemistry
- 일반주제명
- Energy
- 일반주제명
- Physical properties
- 기타저자
- California Institute of Technology Engineering and Applied Science
- 기본자료저록
- Dissertations Abstracts International. 87-01B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798290651286
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■035 ▼a(MiAaPQ)Caltech17285
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a004
■1001 ▼aKing, Robbie.
■24510▼aAdvancing Applications of Quantum Computers in Quantum Simulation, Optimization, Learning, and Topological Data Analysis
■260 ▼a[Sl]▼bCalifornia Institute of Technology▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a240 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-01, Section: B.
■500 ▼aAdvisor: Schulman, Leonard;Vidick, Thomas.
■5021 ▼aThesis (Ph.D.)--California Institute of Technology, 2025.
■520 ▼aThis thesis investigates novel directions for harnessing the potential of quantum computers in future applications. It is structured into three sections.Quantum Simulation.We address two key questions: what systems exhibit quantum advantage in predicting ground state properties, and how can we reduce the cost of quantum simulations? For the former, we find that strongly interacting fermionic systems have promising characteristics for quantum advantage. For the latter, we develop an improved method for compiling block encodings using sum-of-squares optimization.Learning with EntangledMeasurements. We explore the benefits of leveraging entangled measurements on quantum states stored in quantum memory. These learning algorithms can be applied to the readout stage of quantum simulations, or to learn from quantum data from nature.Topological Data Analysis.Using complexity-theoretic insights, we demonstrate that certain problems in topological data analysis possess a quantum mechanical structure, suggesting opportunities for quantum algorithms in this area.
■590 ▼aSchool code: 0037.
■650 4▼aQuantum computing
■650 4▼aTomography
■650 4▼aComputers
■650 4▼aPhysics
■650 4▼aPhase transitions
■650 4▼aChemistry
■650 4▼aEnergy
■650 4▼aPhysical properties
■690 ▼a0485
■690 ▼a0791
■690 ▼a0605
■71020▼aCalifornia Institute of Technology▼bEngineering and Applied Science.
■7730 ▼tDissertations Abstracts International▼g87-01B.
■790 ▼a0037
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17358787▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
