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Vignettes on Tensor Network Algorithms: Isometries, State Preparation, and Simulators.
Vignettes on Tensor Network Algorithms: Isometries, State Preparation, and Simulators.
상세정보
- 자료유형
- 학위논문(국외)
- 기본표목-개인명
- 표제와 책임표시사항
- Vignettes on Tensor Network Algorithms: Isometries, State Preparation, and Simulators.
- 발행, 배포, 간사 사항
- 발행, 배포, 간사 사항
- 형태사항
- 182 p.
- 일반주기
- Source: Dissertations Abstracts International, Volume: 87-04, Section: B.
- 일반주기
- Advisor: Zaletel, Michael P.
- 학위논문주기
- Thesis (Ph.D.)--University of California, Berkeley, 2025.
- 요약 등 주기
- 요약Now thirty years since the introduction of the density matrix renormalization group algorithm and the matrix product state ansatz on which it operates, tensor network states have become a ubiquitous tool for the modern many-body theorist. In a manner similar to image compression, tensor networks provide a compressed representation of quantum states with low entanglement and allow for investigation of ground state properties, finite temperature and excitations, and dynamics. However, these algorithms are most robust in one-dimensional chains and limited width two-dimensional cylinders. Spurred by the rapid development of noisy yet increasingly accurate digital quantum computers, there is significant interest in developing tensor network algorithms that enable the study of two-dimensional states and both validate and extend the reach of the experimental devices.Here we present a series of works on tensor network algorithms at the interface of quantum many-body physics and quantum computing. First, we propose extensions to the isometric tensor network ansatz, both increasing its representational power at fixed classical optimization costs and enabling studies of infinite strip geometries. We next demonstrate, using a trapped ion quantum computer, that isometric tensor networks and in particular the multi-scale entanglement renormalization ansatz can be used to holographically prepare prepare critical ground states using a number of qubits logarithmic in system size. Finally, we benchmark a large-scale superconducting quantum computing on a quantum dynamics experiment, demonstrating that the quantum computer extends beyond exact classical simulation and is competitive with state-of-the-art approximate tensor network algorithms.Together, this thesis highlights the advantages of a symbiotic relationship between classical tensor networks and quantum computers and motivates the careful design of novel tensor network algorithms with quantum hardware in mind.
- 주제명부출표목-일반주제명
- 주제명부출표목-일반주제명
- 주제명부출표목-일반주제명
- 비통제 색인어
- 비통제 색인어
- 비통제 색인어
- 비통제 색인어
- 비통제 색인어
- 부출표목-단체명
- 기본자료저록
- Dissertations Abstracts International. 87-04B.
- 전자적 위치 및 접속
- 원문정보보기
MARC
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■1001 ▼aAnand, Sajant.
■24510▼aVignettes on Tensor Network Algorithms: Isometries, State Preparation, and Simulators.
■260 ▼a[S.l.]▼bUniversity of California, Berkeley. ▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a182 p.
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-04, Section: B.
■500 ▼aAdvisor: Zaletel, Michael P.
■5021 ▼aThesis (Ph.D.)--University of California, Berkeley, 2025.
■520 ▼aNow thirty years since the introduction of the density matrix renormalization group algorithm and the matrix product state ansatz on which it operates, tensor network states have become a ubiquitous tool for the modern many-body theorist. In a manner similar to image compression, tensor networks provide a compressed representation of quantum states with low entanglement and allow for investigation of ground state properties, finite temperature and excitations, and dynamics. However, these algorithms are most robust in one-dimensional chains and limited width two-dimensional cylinders. Spurred by the rapid development of noisy yet increasingly accurate digital quantum computers, there is significant interest in developing tensor network algorithms that enable the study of two-dimensional states and both validate and extend the reach of the experimental devices.Here we present a series of works on tensor network algorithms at the interface of quantum many-body physics and quantum computing. First, we propose extensions to the isometric tensor network ansatz, both increasing its representational power at fixed classical optimization costs and enabling studies of infinite strip geometries. We next demonstrate, using a trapped ion quantum computer, that isometric tensor networks and in particular the multi-scale entanglement renormalization ansatz can be used to holographically prepare prepare critical ground states using a number of qubits logarithmic in system size. Finally, we benchmark a large-scale superconducting quantum computing on a quantum dynamics experiment, demonstrating that the quantum computer extends beyond exact classical simulation and is competitive with state-of-the-art approximate tensor network algorithms.Together, this thesis highlights the advantages of a symbiotic relationship between classical tensor networks and quantum computers and motivates the careful design of novel tensor network algorithms with quantum hardware in mind.
■590 ▼aSchool code: 0028.
■650 4▼aCondensed matter physics.
■650 4▼aQuantum physics.
■650 4▼aComputational physics.
■653 ▼aMany-body theory
■653 ▼aQuantum computing
■653 ▼aTensor networks
■653 ▼aQubits
■653 ▼aQuantum hardware
■690 ▼a0611
■690 ▼a0599
■690 ▼a0216
■71020▼aUniversity of California, Berkeley▼bPhysics.
■7730 ▼tDissertations Abstracts International▼g87-04B.
■790 ▼a0028
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17359360▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
