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Pushing the Frontier of Quantum Many-Body Simulation Using Classical Computers
Pushing the Frontier of Quantum Many-Body Simulation Using Classical Computers
상세정보
- 자료유형
- 학위논문 서양
- 최종처리일시
- 20260202104810
- ISBN
- 9798293824397
- DDC
- 530
- 저자명
- Zhou, Yiqing.
- 서명/저자
- Pushing the Frontier of Quantum Many-Body Simulation Using Classical Computers
- 발행사항
- [Sl] : Cornell University, 2025
- 발행사항
- Ann Arbor : ProQuest Dissertations & Theses, 2025
- 형태사항
- 116 p
- 주기사항
- Source: Dissertations Abstracts International, Volume: 87-03, Section: B.
- 주기사항
- Advisor: Kim, Eunah.
- 학위논문주기
- Thesis (Ph.D.)--Cornell University, 2025.
- 초록/해제
- 요약One of the central goals of condensed matter physics is to understand the emergence of collective phenomena in strongly correlated quantum systems. However, the exponential complexity of many-body quantum states poses fundamental challenges to both analytical and classical numerical methods. This thesis explores how classical computation can support and extend the capabilities of quantum simulation, with a focus on both analog and digital quantum simulators, guided by and aligned with recent experimental advancements.The first part of this thesis focuses on analog quantum simulation in solid-state moire materials. In particular, we investigate WSe2/WTe2 heterobilayers as promising platforms for simulating extended Hubbard models on a triangular lattice. Using large-scale density matrix renormalization group (DMRG) simulations, we map out the quantum phase diagram as a function of kinetic energy and Coulomb interactions. Our results identify several exotic phases, including chiral spin liquids and generalized Wigner crystals, and provide theoretical guidance for experimental exploration of interaction-driven quantum phase transitions in these systems.The second part addresses digital quantum simulators and the challenge of quantum error correction. We propose a machine learning-based decoder for fault-tolerant quantum computation in the presence of logical circuits, motivated by the architecture of neutral atom-based quantum processors. By designing a modular neural network architecture and training it on realistic error models, we achieve competitive decoding accuracy and efficiency. Our work highlights the potential of classical machine learning to enhance the performance of quantum error correction, bridging algorithmic design with practical hardware considerations.Together, these studies demonstrate the essential role of classical computation in supporting quantum simulation, from characterizing novel phases in analog platforms to enabling fault tolerance in digital quantum devices. The interplay between theoretical modeling, numerical simulation, and experimental collaboration provides a path forward for probing and harnessing quantum many-body phenomena.
- 일반주제명
- Physics
- 일반주제명
- Applied physics
- 일반주제명
- Quantum physics
- 일반주제명
- Computational physics
- 키워드
- Quantum systems
- 기타저자
- Cornell University Physics
- 기본자료저록
- Dissertations Abstracts International. 87-03B.
- 전자적 위치 및 접속
- 로그인 후 원문을 볼 수 있습니다.
MARC
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■020 ▼a9798293824397
■035 ▼a(MiAaPQ)AAI32166982
■040 ▼aMiAaPQ▼cMiAaPQ
■0820 ▼a530
■1001 ▼aZhou, Yiqing.▼0(orcid)0000-0003-3166-3053
■24510▼aPushing the Frontier of Quantum Many-Body Simulation Using Classical Computers
■260 ▼a[Sl]▼bCornell University▼c2025
■260 1▼aAnn Arbor▼bProQuest Dissertations & Theses▼c2025
■300 ▼a116 p
■500 ▼aSource: Dissertations Abstracts International, Volume: 87-03, Section: B.
■500 ▼aAdvisor: Kim, Eunah.
■5021 ▼aThesis (Ph.D.)--Cornell University, 2025.
■520 ▼aOne of the central goals of condensed matter physics is to understand the emergence of collective phenomena in strongly correlated quantum systems. However, the exponential complexity of many-body quantum states poses fundamental challenges to both analytical and classical numerical methods. This thesis explores how classical computation can support and extend the capabilities of quantum simulation, with a focus on both analog and digital quantum simulators, guided by and aligned with recent experimental advancements.The first part of this thesis focuses on analog quantum simulation in solid-state moire materials. In particular, we investigate WSe2/WTe2 heterobilayers as promising platforms for simulating extended Hubbard models on a triangular lattice. Using large-scale density matrix renormalization group (DMRG) simulations, we map out the quantum phase diagram as a function of kinetic energy and Coulomb interactions. Our results identify several exotic phases, including chiral spin liquids and generalized Wigner crystals, and provide theoretical guidance for experimental exploration of interaction-driven quantum phase transitions in these systems.The second part addresses digital quantum simulators and the challenge of quantum error correction. We propose a machine learning-based decoder for fault-tolerant quantum computation in the presence of logical circuits, motivated by the architecture of neutral atom-based quantum processors. By designing a modular neural network architecture and training it on realistic error models, we achieve competitive decoding accuracy and efficiency. Our work highlights the potential of classical machine learning to enhance the performance of quantum error correction, bridging algorithmic design with practical hardware considerations.Together, these studies demonstrate the essential role of classical computation in supporting quantum simulation, from characterizing novel phases in analog platforms to enabling fault tolerance in digital quantum devices. The interplay between theoretical modeling, numerical simulation, and experimental collaboration provides a path forward for probing and harnessing quantum many-body phenomena.
■590 ▼aSchool code: 0058.
■650 4▼aPhysics
■650 4▼aApplied physics
■650 4▼aQuantum physics
■650 4▼aComputational physics
■653 ▼aQuantum systems
■653 ▼aDensity matrix renormalization group
■653 ▼aQuantum error correction
■653 ▼aFault-tolerant quantum computation
■653 ▼aDigital quantum devices
■690 ▼a0605
■690 ▼a0599
■690 ▼a0800
■690 ▼a0215
■690 ▼a0216
■71020▼aCornell University▼bPhysics.
■7730 ▼tDissertations Abstracts International▼g87-03B.
■790 ▼a0058
■791 ▼aPh.D.
■792 ▼a2025
■793 ▼aEnglish
■85640▼uhttp://www.riss.kr/pdu/ddodLink.do?id=T17358925▼nKERIS▼z이 자료의 원문은 한국교육학술정보원에서 제공합니다.
