Harnessing Quantum Computational Methods, Tensor Networks, and Machine Learning for Advanced Simulations in Quantum Field Theories
利用量子计算方法、张量网络和机器学习进行量子场论的高级模拟
基本信息
- 批准号:2876830
- 负责人:
- 金额:--
- 依托单位:
- 依托单位国家:英国
- 项目类别:Studentship
- 财政年份:2023
- 资助国家:英国
- 起止时间:2023 至 无数据
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
The objective of this PhD project is to create a robust framework for simulating quantum field theories (QFTs) by integrating quantum computational methods, tensor network theories (TNT), and machine learning (ML) techniques. This interdisciplinary endeavour aims to mitigate computational challenges inherent in classical simulations of QFTs, paving the way for deeper insights into fundamental physics and high-energy phenomena. Quantum algorithms tailored for QFT simulations on quantum hardware will be developed alongside quantum-classical hybrid algorithms to harness both computational paradigms. The project will implement tensor network decomposition methods to efficiently represent and manipulate states and operators in QFTs, exploring the entanglement structures and devising efficient algorithms for simulating low-dimensional QFTs. Machine learning techniques will be employed to optimize tensor network structures and quantum circuits, as well as for error mitigation to enhance the robustness and accuracy of quantum simulations. Benchmarking against classical methods and existing quantum simulation approaches will validate the developed frameworks. Performance optimization for different quantum hardware architectures will be carried out to investigate the scalability and real and near-term quantum computer performance. The expected outcomes include an optimized framework for QFT simulations leveraging quantum computing, tensor networks, and ML, benchmark results showcasing the performance and accuracy against classical methods, and new insights into the entanglement structure of QFTs. This project has the potential to significantly influence the way QFT simulations are conducted, fostering further innovations at the nexus of quantum computing, machine learning, and high-energy physics. Moreover, the project could be extended to explore applications in other physics areas like condensed matter physics or quantum gravity and employ advanced ML techniques like deep learning or reinforcement learning for further optimization of the simulation framework. The candidate will engage in a cutting-edge interdisciplinary field with extensive potential for theoretical and practical advancements in quantum computing and high-energy physics through this project.
这个博士项目的目标是通过整合量子计算方法,张量网络理论(TNT)和机器学习(ML)技术来创建一个强大的框架来模拟量子场论(QFT)。这项跨学科的努力旨在减轻QFT经典模拟中固有的计算挑战,为深入了解基础物理和高能现象铺平道路。为量子硬件上的QFT模拟量身定制的量子算法将与量子经典混合算法一起开发,以利用这两种计算范式。该项目将实施张量网络分解方法,以有效地表示和操纵QFT中的状态和算子,探索纠缠结构并设计用于模拟低维QFT的有效算法。机器学习技术将用于优化张量网络结构和量子电路,以及减少错误,以提高量子模拟的鲁棒性和准确性。对经典方法和现有的量子模拟方法进行基准测试将验证所开发的框架。针对不同的量子硬件架构进行性能优化,以研究可扩展性以及真实的和近期的量子计算机性能。预期成果包括利用量子计算、张量网络和ML的QFT模拟优化框架,展示与经典方法相比性能和准确性的基准测试结果,以及对QFT纠缠结构的新见解。该项目有可能显著影响QFT模拟的进行方式,促进量子计算、机器学习和高能物理学的进一步创新。此外,该项目可以扩展到探索其他物理领域的应用,如凝聚态物理或量子引力,并采用先进的机器学习技术,如深度学习或强化学习,以进一步优化模拟框架。候选人将通过该项目从事尖端的跨学科领域,在量子计算和高能物理的理论和实践方面具有广泛的潜力。
项目成果
期刊论文数量(0)
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专利数量(0)
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其他文献
Internet-administered, low-intensity cognitive behavioral therapy for parents of children treated for cancer: A feasibility trial (ENGAGE).
针对癌症儿童父母的互联网管理、低强度认知行为疗法:可行性试验 (ENGAGE)。
- DOI:
10.1002/cam4.5377 - 发表时间:
2023-03 - 期刊:
- 影响因子:4
- 作者:
- 通讯作者:
Differences in child and adolescent exposure to unhealthy food and beverage advertising on television in a self-regulatory environment.
在自我监管的环境中,儿童和青少年在电视上接触不健康食品和饮料广告的情况存在差异。
- DOI:
10.1186/s12889-023-15027-w - 发表时间:
2023-03-23 - 期刊:
- 影响因子:4.5
- 作者:
- 通讯作者:
The association between rheumatoid arthritis and reduced estimated cardiorespiratory fitness is mediated by physical symptoms and negative emotions: a cross-sectional study.
类风湿性关节炎与估计心肺健康降低之间的关联是由身体症状和负面情绪介导的:一项横断面研究。
- DOI:
10.1007/s10067-023-06584-x - 发表时间:
2023-07 - 期刊:
- 影响因子:3.4
- 作者:
- 通讯作者:
ElasticBLAST: accelerating sequence search via cloud computing.
ElasticBLAST:通过云计算加速序列搜索。
- DOI:
10.1186/s12859-023-05245-9 - 发表时间:
2023-03-26 - 期刊:
- 影响因子:3
- 作者:
- 通讯作者:
Amplified EQCM-D detection of extracellular vesicles using 2D gold nanostructured arrays fabricated by block copolymer self-assembly.
使用通过嵌段共聚物自组装制造的 2D 金纳米结构阵列放大 EQCM-D 检测细胞外囊泡。
- DOI:
10.1039/d2nh00424k - 发表时间:
2023-03-27 - 期刊:
- 影响因子:9.7
- 作者:
- 通讯作者:
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{{ truncateString('', 18)}}的其他基金
An implantable biosensor microsystem for real-time measurement of circulating biomarkers
用于实时测量循环生物标志物的植入式生物传感器微系统
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2896097 - 财政年份:2027
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Likelihood and impact of severe space weather events on the resilience of nuclear power and safeguards monitoring.
严重空间天气事件对核电和保障监督的恢复力的可能性和影响。
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2908918 - 财政年份:2027
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质子、α 和 γ 辐照辅助应力腐蚀开裂:了解燃料-不锈钢界面
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2908693 - 财政年份:2027
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Field Assisted Sintering of Nuclear Fuel Simulants
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2908917 - 财政年份:2027
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评估用于航空航天应用的新型抗疲劳钛合金
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使用右旋糖酐-胶原蛋白水凝胶开发 3D 打印皮肤模型,以分析白细胞介素 17 抑制剂的细胞和表观遗传效应
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了解野生鸟类肠道微生物组、行为和城市化之间的相互作用
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