Efficient Quantum State Preparation Algorithms

高效的量子态制备算法

• 批准号: 2310620
• 负责人: Dean Lee
• 金额: $ 25.6万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2310620&HistoricalAwards=false
• 关键词: Efficient; Quantum; State; Preparation; Algorithms

Quantum computing has the potential to address many of the unsolved problems of quantum many-body physics. However, there are serious inefficiencies that must be addressed. In this project, several methods are explored to improve the performance of quantum computing algorithms for quantum state preparation. These include preconditioning methods that increase the probability of success, sampling methods for the density of energy levels, and the reduction of systematic errors due to time step size. As part of the broader impacts of this research proposal, inspiring and diverse public speakers in the field of quantum computing and applications will be invited to present public talks in the Advanced Studies Gateway series.The rodeo algorithm is an efficient quantum computing algorithm that can prepare general eigenstates of quantum Hamiltonians. In this project, the 2D Heisenberg model is used as a benchmark system to develop several methods to accelerate and improve the performance of the rodeo algorithm. The block partition method and the variational rodeo algorithm, two different methods for preconditioning the initial state, will be tested. Stochastic methods with finite energy resolution will then be used to determine the full energy spectrum. Trotter step adiabatic evolution will be used to perform extrapolations to small Trotter step size.This award by the Quantum Information Science program is jointly supported by the Theoretical Nuclear Physics in the Division of Physics within the Directorate for Mathematical and Physical Sciences.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.

量子计算有潜力解决量子多体物理中许多未解决的问题。 然而，必须解决严重的效率低下问题。 在该项目中，探索了多种方法来提高用于量子态制备的量子计算算法的性能。 其中包括增加成功概率的预处理方法、能级密度的采样方法以及减少由于时间步长而导致的系统误差。作为该研究计划更广泛影响的一部分，量子计算和应用领域鼓舞人心且多样化的公众演讲者将被邀请在高级研究门户系列中发表公开演讲。 Rodeo 算法是一种高效的量子计算算法，可以准备量子哈密顿量的一般本征态。 在该项目中，以二维海森堡模型为基准系统，开发了多种方法来加速和提高 Rodeo 算法的性能。将测试块划分方法和变分圈地算法这两种不同的预处理初始状态的方法。然后将使用具有有限能量分辨率的随机方法来确定全能谱。 Trotter 步绝热演化将用于对小 Trotter 步长进行外推。该奖项由量子信息科学计划获得，并得到数学和物理科学理事会物理部理论核物理联合支持。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。