Extracting Spectral Information from Noisy Quantum Data

从噪声量子数据中提取光谱信息

• 批准号: 2310182
• 负责人: Emanuel Gull
• 金额: $ 35万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 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: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2310182&HistoricalAwards=false
• 关键词: Extracting; Spectral; Information; Noisy; Quantum

Quantum computing describes an innovative approach to computing that utilizes the principles of quantum mechanics to solve problems in science and engineering. Existing implementations of quantum computers are highly susceptible to unwanted external disturbances, known as “decoherence”, which result in the loss of quantum information. This decoherence poses a significant constraint on the range of problems that can be simulated effectively on quantum computers. The limitation is especially severe in the simulation of properties of physically relevant and technologically significant quantum mechanical systems, such as molecules or solids. However, substantial knowledge exists regarding the mathematical and physical properties of these systems. This project aims to leverage this insight to improve the accuracy of quantum mechanical simulations on quantum computers. It will do so by designing precise and practical methodologies for reducing noise and improving accuracy, thereby promoting the progress of science.All quantum computers built so far suffer from noise and decoherence issues. When quantum computers are used to simulate the properties of molecules and solids in condensed matter and quantum chemistry, the main property of interest is the excitation information encoded in the spectra of response functions. This spectral information has mathematical properties that severely constrain the allowed response functions, and that can therefore be employed as a ‘noise filter’ for quantum computing data. This project will investigate ways to employ this mathematical information in practical algorithms to reduce noise of quantum data. Implementations and tests with synthetic data and with data from present-day quantum computers are proposed. In addition, an outreach collaboration with the University of Michigan’s Museum of Natural History will introduce the public at schools and libraries to quantum phenomena including quantum coherence.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.

量子计算描述了一种利用量子力学原理解决科学和工程问题的创新计算方法。量子计算机的现有实现非常容易受到不必要的外部干扰，称为“退相干”，这会导致量子信息的丢失。这种退相干对可以在量子计算机上有效模拟的问题的范围构成了重大限制。在模拟物理相关和技术重要的量子力学系统（如分子或固体）的性质时，这种限制尤其严重。然而，大量的知识存在关于这些系统的数学和物理性质。该项目旨在利用这一洞察力来提高量子计算机上量子力学模拟的准确性。它将通过设计精确而实用的方法来降低噪声和提高精度，从而促进科学的进步。迄今为止建造的所有量子计算机都存在噪声和退相干问题。当量子计算机用于模拟凝聚态和量子化学中分子和固体的性质时，感兴趣的主要性质是编码在响应函数光谱中的激发信息。这种光谱信息具有严格限制允许的响应函数的数学特性，因此可以用作量子计算数据的“噪声滤波器”。该项目将研究如何在实际算法中使用这些数学信息来减少量子数据的噪声。提出了使用合成数据和来自当今量子计算机的数据的实现和测试。此外，与密歇根大学自然历史博物馆的外联合作将向学校和图书馆的公众介绍量子现象，包括量子相干性。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。