Collaborative Research: FET: Medium: Robust Quantum Networks via Efficient Entanglement Distribution

量子计算有潜力，如果实现，将显著改变计算领域。然而，建造大规模量子计算机是一个关键挑战。量子网络（QNs）通过连接较小的量子计算机，可以构建大型、健壮且功能更强的量子计算平台。网络量子系统有可能通过导致医学和工程的更快发展来显着改变社会中的各种活动；更安全和保护隐私的通信；利用巨大的计算能力来识别制造、物流、金融等领域的效率，实现迄今为止不可行的优化。该项目还利用量子网络的潜力和吸引力来设计和提供各种教育项目，包括灵活的量子计算和网络学士学位后项目，以满足非传统学生的需求，提高本科生和研究生群体的多样性，并培养有量子能力的劳动力。构建支持跨节点健壮通信的量子网络需要几个基本的科学和技术进步，特别是因为经典技术不能直接用于量子机制。量子网络可以用来构建比独立量子计算机更强大、更有弹性的量子计算系统。该项目通过开发网络中有效通信和管理量子纠缠的基础设施，从头开始研究量子网络的设计和实现。此外，该项目正在解决量子网络的两个关键应用中的具体挑战：(i)分布式量子算法和（ii）量子传感器网络。该项目正在使用大规模模拟和遍布纽约长岛的6节点QN测试平台来评估开发的技术。该试验台为评估我们开发的技术的有效性提供了一个高保真的平台。总的来说，该项目有三个研究重点。在第一阶段，该项目正在开发一个基础设施，以促进有效的通信和纠缠管理。特别是，它正在开发优化技术，用于(i)使用多路径有效地产生长距离纠缠，以及（ii）有效地分配预分布纠缠。此外，该项目正在开发在实际环境中有效的纠缠蒸馏策略，以及多播原语的协议。在第二个重点中，该项目正在解决两个关键QN应用背景下的挑战，以证实和验证开发的技术。特别是，该项目正在开发集中式量子电路高效分布式实现的优化技术；高效的分布式实现对于QN的计算成功至关重要。在量子传感器网络的背景下，它正在设计用于估计二进制参数函数的有效协议，并研究在这些设置中纠缠的好处。在第三个推力中，该项目正在使用大规模模拟和小型QN试验台评估上述技术。为了有效地评估QN的性能，该项目正在为QN制定新的性能指标；这需要经典网络度量的非平凡泛化。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。

Quantum Computing has the potential, if realized, to significantly alter the computing landscape. However, building large-scale quantum computers is a key challenge. Quantum Networks (QNs) enable the construction of large, robust, and more capable quantum-computing platforms by connecting smaller quantum computers. Networked quantum systems have the potential to significantly alter various activities in society by leading to faster development in medicine and engineering; more secure and privacy-preserving communication; and hitherto infeasible optimizations that leverage the immense computational power to identify efficiencies in manufacturing, logistics, finance, etc. This project is also using the potential and attractiveness of QNs to design and offer a variety of educational programs, including a flexible post-baccalaureate program in quantum computing and networking to cater to non-traditional students, improve the diversity of undergraduate and graduate student body, and develop a quantum capable workforce.Building QNs that support robust communication across nodes requires several fundamental scientific and technological advances, especially since classical techniques cannot be directly used in the quantum regime. QNs can be used to build quantum computing systems that are more capable and more resilient than stand-alone quantum computers. This project is examining the design and implementation of QNs from the ground up by developing an infrastructure for efficient communication and management of quantum entanglements in the network. In addition, the project is addressing specific challenges in two key applications of QNs: (i) Distributed quantum algorithms, and (ii) Quantum sensor networks. The project is evaluating the developed techniques using large-scale simulations and over a 6-node QN testbed spread across Long Island, NY. The testbed is providing a high-fidelity platform to evaluate the effectiveness of our developed techniques. Overall, the project has three research thrusts.In the first thrust, the project is developing an infrastructure to facilitate efficient communication and entanglement management. In particular, it is developing optimization techniques for (i) efficient generation of long-distance entanglement using multiple paths, and (ii) efficient distribution of pre-distributed entanglements. In addition, the project is developing efficient entanglement-distillation strategies in practical settings, and protocols for multicast primitives. In the second thrust, the project is addressing challenges in the context of two key QN applications to corroborate and validate the developed techniques. In particular, the project is developing optimization techniques for efficient distributed implementation of centralized quantum circuits; efficient distributed implementations are important for QN’s computational success. In the context of quantum sensor networks, it is designing efficient protocols for the estimation of binary parameter functions and investigating the benefit of entanglements in these settings. In the third thrust, the project is evaluating the above techniques using large-scale simulations and a small QN testbed. To evaluate QN performance effectively, the project is formulating novel performance metrics for QNs; this requires non-trivial generalization of the classical network metrics.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.