Finite Temperature Simulation of Non-Markovian Quantum Dynamics in Condensed Phase using Quantum Computers

使用量子计算机对凝聚相非马尔可夫量子动力学进行有限温度模拟

基本信息

批准号：
2320328
负责人：
Fei Wang
金额：
$ 50.53万
依托单位：
George Mason University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2023
资助国家：
美国
起止时间：
2023-06-01 至 2026-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2320328&HistoricalAwards=false
关键词：
Finite Temperature Simulation Non Markovian

项目摘要

With support from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry, Fei Wang of George Mason University will work to develop efficient quantum algorithms to perform condensed phase quantum dynamics simulations on quantum computers. Many important physical and chemical processes occur in the condensed phase, spanning chemical reactions in solutions, charge transfer at semiconductor interfaces, and solar energy conversion in molecular aggregates. The scientific investigation of these processes not only promotes our fundamental understanding but also offers practical solutions to materials design and environmental sustainability. As many of these processes involve charge migration and excitation energy transfer, quantum dynamics involving many degrees of freedom is essential for their description. However, such simulations are resource intensive on classical computers. On the other hand, quantum computers are naturally suited for quantum simulations. With algorithm development, Dr. Wang is aiming to show quantum speedup for quantum dynamics simulations in condensed phases, and demonstrate practical applications of quantum computing in the area of quantum simulation. Graduate students and postdoctoral researchers involved in this project will receive rigorous training in quantum information science and master state-of-the-art quantum simulation tools. Through internship programs offered to undergraduate and high school students, the PI will support underrepresented and economically disadvantaged groups. These efforts will not only encourage broad participation in STEM (science, technology, engineering and mathematics), but also help to educate a future quantum workforce for careers in academia and industry. The focus of this work will be to develop efficient quantum algorithms for finite-temperature non-Markovian time evolution, which offers a general framework for condensed phase quantum dynamics. New advances in this project will cover unitary operator construction, efficient quantum circuit compilation, model and real system simulations, and performance comparison between different types of quantum devices. Three mathematical methods will be explored (unitary dilation, singular value decomposition, and linear combinations of unitary operators) for non-unitary to unitary conversion, and their effectiveness will be assessed based on complexity theory. Two general approaches will be investigated for circuit compilation: one performs the exact mathematical decomposition, and the other uses the variational quantum circuit method. The optimal circuit structure will be identified based on gate counts and circuit depth. The algorithm will be tested on spin-boson models as well as on realistic systems, and the performance of trapped ions and superconducting devices will be compared. The success of the algorithm will offer quantum speedup in simulations of multi-state non-Markovian quantum dynamics at finite temperature. A user-friendly and open-source platform will be put forward such that, with input parameters, dynamical simulations on a quantum computer can be carried out and the results analyzed. This work could potentially inspire future quantum algorithm design for simulating the dynamics of open quantum systems.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在化学系化学理论、模型和计算方法项目的支持下，乔治梅森大学的王飞将致力于开发高效的量子算法，以在量子计算机上进行凝聚相量子动力学模拟。许多重要的物理和化学过程发生在凝聚相中，包括溶液中的化学反应、半导体界面上的电荷转移以及分子聚集体中的太阳能转换。对这些过程的科学研究不仅促进了我们的基本理解，而且还为材料设计和环境可持续性提供了实用的解决方案。由于许多过程涉及电荷迁移和激发能量转移，因此涉及多个自由度的量子动力学对于它们的描述至关重要。然而，这种模拟在传统计算机上是资源密集型的。另一方面，量子计算机天然适合量子模拟。通过算法开发，王博士的目标是展示凝聚相中量子动力学模拟的量子加速，并展示量子计算在量子模拟领域的实际应用。参与该项目的研究生和博士后研究人员将接受严格的量子信息科学培训并掌握最先进的量子模拟工具。通过为本科生和高中生提供实习计划，PI 将支持代表性不足和经济弱势群体。这些努力不仅将鼓励人们广泛参与 STEM（科学、技术、工程和数学），而且有助于培养未来学术界和工业界的量子劳动力。这项工作的重点将是开发用于有限温度非马尔可夫时间演化的有效量子算法，这为凝聚相量子动力学提供了通用框架。该项目的新进展将涵盖酉算子构建、高效量子电路编译、模型和真实系统模拟以及不同类型量子器件之间的性能比较。将探索用于非酉到酉转换的三种数学方法（酉膨胀、奇异值分解和酉算子的线性组合），并基于复杂性理论评估它们的有效性。将研究两种通用的电路编译方法：一种执行精确的数学分解，另一种使用变分量子电路方法。将根据门数和电路深度来确定最佳电路结构。该算法将在自旋玻色子模型以及现实系统上进行测试，并对捕获离子和超导器件的性能进行比较。该算法的成功将为有限温度下多态非马尔可夫量子动力学的模拟提供量子加速。将提出一个用户友好的开源平台，通过输入参数，可以在量子计算机上进行动态模拟并分析结果。这项工作可能会激发未来模拟开放量子系统动力学的量子算法设计。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。