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Collaborative Research: EAGER-QSA: Variational Monte-Carlo-Inspired Quantum Algorithms for Many-Body Systems and Combinatorial Optimization

合作研究：EAGER-QSA：用于多体系统和组合优化的变分蒙特卡罗量子算法

基本信息

批准号：
2038030
负责人：
Shravan Veerapaneni
金额：
$ 15万
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：
2020
资助国家：
美国
起止时间：
2020-09-01 至 2024-02-29
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2038030&HistoricalAwards=false
关键词：
Collaborative Research EAGER QSA Variational

项目摘要

Simulation of many-body quantum systems is an extremely challenging scientific problem that pushes the limits of existing and foreseeable classical computing capabilities. The ability to simulate such systems efficiently would open the possibility of resolving longstanding scientific questions in high-temperature superconductivity as well as facilitating the design of new drugs and materials. General-purpose solvers for quantum systems in greater than one spatial dimension are beyond the reach of classical hardware and are natural candidate problems for exploiting quantum resources. This project investigates how recent progress in variational Monte Carlo simulation can be used to inspire new quantum simulation techniques. The project will also undertake educational, mentoring, and outreach activities that are integrated with the research effort. This project will capitalize on recent intellectual bridges formed between the fields of variational quantum algorithms, variational Monte Carlo methods, and quantum information geometry. It consists of three research thrusts focused on fermionic systems simulation, combinatorial optimization, and quantum information geometry. Specifically, this project will design variational quantum algorithms that avoid nonlocal fermion-qubit mappings by exploiting first-quantized and gauge-theoretic reformulations of fermionic systems. Efficient methods for imposing constraints in quantum-inspired solvers for combinatorial optimization problems will be explored. Building upon classical numerical analysis tools, acceleration techniques for the quantum natural gradient will be developed by exploiting higher-order invariance of the Riemannian metric. Because the real-time evolution process by which physical states evolve according to the Schrodinger equation is of fundamental importance, the project will furthermore investigate generalization of the quantum natural gradient from imaginary to real time.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.

多体量子系统的模拟是一个极具挑战性的科学问题，它推动了现有和可预见的经典计算能力的极限。有效模拟这种系统的能力将为解决高温超导性中长期存在的科学问题以及促进新药物和材料的设计提供可能性。在大于一个空间维度上的量子系统的通用求解器超出了经典硬件的范围，并且是利用量子资源的自然候选问题。本项目研究如何在变分蒙特卡罗模拟的最新进展可以用来激发新的量子模拟技术。该项目还将开展与研究工作相结合的教育、指导和外联活动。这个项目将利用最近形成的变分量子算法，变分蒙特卡罗方法和量子信息几何领域之间的智力桥梁。它包括三个研究重点集中在费米系统模拟，组合优化和量子信息几何。具体来说，这个项目将设计变分量子算法，通过利用费米子系统的第一量子化和规范理论重新表述来避免非局域费米子量子比特映射。将探讨在量子启发的组合优化问题求解器中施加约束的有效方法。建立在经典的数值分析工具，加速技术的量子自然梯度将开发利用高阶不变性的黎曼度量。由于物理状态根据薛定谔方程演化的实时演化过程至关重要，该项目将进一步研究从虚时间到真实的时间的量子自然梯度的推广。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。

项目成果

期刊论文数量（6）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Scalable neural quantum states architecture for quantum chemistry

用于量子化学的可扩展神经量子态架构

DOI：
10.1088/2632-2153/acdb2f
发表时间：
2023
期刊：
Machine Learning: Science and Technology
影响因子：
0
作者：
Zhao, Tianchen;Stokes, James;Veerapaneni, Shravan
通讯作者：
Veerapaneni, Shravan

Toward Neural Network Simulation of Variational Quantum Algorithms

变分量子算法的神经网络模拟

DOI：
发表时间：
2022
期刊：
NeurIPS 2022 AI for Science: Progress and Promises
影响因子：
0
作者：
Oliver Knitter;James Stokes;Shravan Veerapaneni
通讯作者：
Shravan Veerapaneni

Continuous-variable neural network quantum states and the quantum rotor model

DOI：
10.1007/s42484-023-00100-9
发表时间：
2021-07
期刊：
Quantum Machine Intelligence
影响因子：
4.8
作者：
J. Stokes;Saibal De;S. Veerapaneni;Giuseppe Carleo
通讯作者：
J. Stokes;Saibal De;S. Veerapaneni;Giuseppe Carleo

Overcoming Barriers to Scalability in Variational Quantum Monte Carlo

DOI：
10.1145/3458817.3476219
发表时间：
2021-06
期刊：
SC21: International Conference for High Performance Computing, Networking, Storage and Analysis
影响因子：
0
作者：
Tianchen Zhao-;Saibal De;Brian Chen;J. Stokes;S. Veerapaneni
通讯作者：
Tianchen Zhao-;Saibal De;Brian Chen;J. Stokes;S. Veerapaneni

Meta-variational quantum Monte Carlo

元变分量子蒙特卡罗