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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.

埃默里大学的Francesco Evangelista教授得到了化学部化学理论、模型和计算计划的支持，以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，这些量子计算机在许多任务上可能比经典计算机具有巨大的优势。量子计算机的一个特别有前途的应用是模拟电子和原子核：原子、分子和材料的基本成分。关于分子的性质和反应方式的许多悬而未决的问题，即使使用速度最快的超级计算机也无法回答。量子计算机甚至有可能解决最具挑战性的化学问题。实现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。该项目的更广泛影响包括组织一所冬季学校，培训广泛而多样化的一代量子计算研究人员和教育工作者。该项目还支持实现这些新算法的开源计算机代码的开发。该项目探索变分量子本征分解方法的自适应版本。这些方法已经被证明可以产生非常紧凑的量子电路。虽然在这方面取得了成功，但目前的方法在基于近期量子计算机的应用中是不切实际的，因为它们需要大量的测量。针对目前自适应变分量子算法测量代价高的问题，提出了一种基于机器学习的新策略。变分量子算法中紧凑量子电路的选择被描述为一个博弈，其目标是用较少的量子门找到最优的变分解。更根本的是，这个项目探索了生成机器学习的量子电路的方法，这些电路最适合计算问题的特定实例。因此，它可能导致一种广泛适用于量子信息科学中其他问题的方法，在量子信息科学中，紧凑型量子电路是关键。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。