EAGER: QAC-QSA: Can classical machine learning beat variational quantum algorithms at their own game?

EAGER：QAC-QSA：经典机器学习能否在自己的游戏中击败变分量子算法？

基本信息

批准号：
2038019
负责人：
Francesco Evangelista
金额：
$ 29.28万
依托单位：
Emory University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2023-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2038019&HistoricalAwards=false
关键词：
EAGER QAC QSA classical machine

项目摘要

Professor Francesco Evangelista of Emory University is supported by an award from the Chemical Theory, Models and Computation Program in the Division of Chemistry to develop algorithms that combine machine learning and quantum computing. Quantum computers carry out computations using the principles of quantum mechanics and these quantum computers may have an enormous advantage over classical computers in many tasks. A particularly promising application of quantum computers is the simulation of electrons and nuclei: the elementary constituents of atoms, molecules, and materials. Many open questions regarding the properties and the ways molecules react cannot be answered even using the fastest supercomputers. Quantum computers can potentially address even the most challenging chemistry problems. Realizing this potential requires the development of practical quantum algorithms for molecular simulations. Professor Evangelista develops methods that combine classical machine learning with quantum algorithms to create more efficient ways to perform molecular simulations. This project's broader impacts include organizing a winter school to train a broad and diverse generation of researchers and educators in quantum computing. The project also supports the development of open-source computer codes that implement these new algorithms.This project explores adaptive versions of the variational quantum eigensolver method. These approaches have been demonstrated to produce very compact quantum circuits. While successful in this regard, the current approaches are impractical in applications based on near-term quantum computers due to the high number of measurements they require. A new strategy is pursued based on machine learning to avoid the high measurement cost of current adaptive variational quantum algorithms. The selection of a compact quantum circuit for variational quantum algorithms is formulated as a game in which the goal is to find the best variational solution with fewer quantum gates. More fundamentally, this project explores ways to generate machine-learned quantum circuits optimal for a specific instance of a computational problem. Therefore, it could lead to an approach broadly applicable to other problems in quantum information science, where compact quantum circuits are sought.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.

埃默里大学（Emory University）的Francesco Evangelista教授得到了化学理论，模型和计算计划的奖项，用于开发结合机器学习和量子计算的算法。量子计算机使用量子力学原理进行计算，而这些量子计算机在许多任务中可能比古典计算机具有巨大的优势。量子计算机的一种特别有希望的应用是电子和核的模拟：原子，分子和材料的基本成分。关于属性以及分子反应的方式，即使使用最快的超级计算机也无法回答分子反应的方式。量子计算机甚至可以解决最具挑战性的化学问题。意识到这一潜力需要开发用于分子模拟的实用量子算法。 Evangelista教授开发了将经典的机器学习与量子算法相结合的方法，以创建更有效的方法来执行分子模拟。该项目的更广泛的影响包括组织一所冬季学校来培训量子计算的广泛而多样的研究人员和教育者。该项目还支持实施这些新算法的开源计算机代码的开发。本项目探讨了变异量子eigensolver方法的自适应版本。这些方法已被证明可以产生非常紧凑的量子电路。尽管在这方面取得了成功，但由于所需的测量数量大量，基于近期量子计算机的应用中，当前的方法是不切实际的。基于机器学习，采取了一种新策略，以避免当前自适应变分量子算法的高测量成本。选择用于变异量子算法的紧凑型量子电路是作为游戏制定的，其目标是找到具有较少量子门的最佳变分解决方案。从根本上讲，该项目探讨了为计算问题的特定实例生成机器学习的量子电路的方法。因此，这可能导致一种方法广泛适用于寻求紧凑型量子电路的其他问题。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子和更广泛影响的评估审查标准来通过评估来获得支持的。