Collaborative Research: EAGER: QIA: Large Scale QAOA Quantum Simulator

本课题的目标是开发一个专门的量子近似优化算法（QAOA）量子电路模拟器。QAOA是研究最多的量子优化算法，被认为是展示量子优势的首选算法。全球顶级量子信息科学研究人员正在进行一场竞赛，以寻找在量子设备上而不是在经典计算机上运行得更高效、更快的组合优化问题及其实例。在经典计算机上更快地找到电路参数以加速变分量子-经典框架是关键的瓶颈之一。所开发的模拟器的预期改进将显著提高QAOA仿真的速度，至少提高一个数量级，并显著加快寻找最佳QAOA电路参数的研究。因此，它将有助于美国科学家在这个竞争激烈的科学领域实现量子优势。该项目的技术目标是在可扩展量子模拟器的开发中，对张量收缩的各种节点消除方法进行计算和算法研究。该计划是建立在组合科学计算社区在开发消除算法方面的成功基础上，这些算法用于最小化矩阵计算中的误差和复杂性，优化自动微分中的时间和空间复杂性，以及树宽度优化算法的最新发展。此外，在本项目中，重点将放在扩展相关图算法上，以实现大规模量子模拟器可接受的时间/质量权衡。本项目的算法和软件产品将包括一个专门的QAOA量子电路开源模拟器，该模拟器配备了快速优化算法来加速张量收缩方法。一个高质量的模拟器可以扩展到足够大的电路，这是发现量子优势应用的主要瓶颈之一。该奖项反映了美国国家科学基金会的法定使命，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。

The goal of this project is to develop a specialized Quantum Approximate Optimization Algorithm (QAOA) quantum circuit simulator. QAOA is the most studied quantum optimization algorithm and is considered to be the prime candidate for demonstrating quantum advantage. There is a worldwide race underway amongst top quantum information science researchers to find combinatorial optimization problems and their instances that run efficiently and faster on quantum devices rather than on classical computers. One of the critical bottlenecks is to find circuit parameters faster on a classical computer to accelerate variational quantum-classical frameworks. The expected improvements to the developed simulator will dramatically increase the speed of QAOA simulations by at least one order of magnitude and significantly speed up research done on finding optimal QAOA circuit parameters. As a result, it will help the realization of quantum advantage by US scientists in this highly competitive field of science.The technical goal of this project is to carry out computational and algorithmic investigations in various node elimination methods for tensor contraction in the development of a scalable quantum simulator. The plan is to build upon the success of the combinatorial scientific computing community in developing elimination algorithms for such tasks as minimizing the error and complexity in matrix computations and optimizing the time and space complexity in automatic differentiation, and recent developments in treewidth optimization algorithms. Additionally, in this project, emphasis will be placed on scaling up relevant graph algorithms to achieve acceptable time/quality trade-off for large-scale quantum simulators. Algorithmic and software products of this project will include a specialized QAOA quantum circuit open-source simulator equipped with fast optimization algorithms to accelerate tensor contraction methods. A high-quality simulator that scales to sufficiently large circuits is one of the major bottlenecks in discovering applications for demonstrating quantum advantage.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.