SBIR Phase II: A Cloud-Based Development Framework and Tool Suite for Quantum Computing

这个小企业创新研究（SBIR）第二阶段项目的更广泛的影响/商业潜力将使廉价的量子计算（QC）成为可能，并降低编程和应用程序托管任务的复杂性，这些任务目前对潜在用户构成了主要的进入障碍。QC技术预计将破坏高性能计算环境的重要部分，以解决优化问题，而以前的特点是缓慢而渐进的性能改进。该项目将提供一个平台，既提高了分析复杂优化问题的效率，又降低了分析复杂优化问题的成本，这可能会在解决此类问题的广泛经济领域刺激快速创新。这个小企业创新研究（SBIR）第二阶段项目解决了使用QC技术的基于云的平台的需求。早期的量子计算机已经由多个硬件供应商推出。尽管QC处理器在性能上取得了进步，但很少有人致力于开发编程环境和应用程序，这些环境和应用程序可以提供简单而廉价的QC功能访问，并且可以利用QC系统在不久的将来拥有的功能。该项目将开发一套前端和后端工具，有效地将高级计算问题转化为电路模型QC系统的公式，抽象出构建QC应用程序所需的物理低级细节和领域知识。该项目将进一步开发优化、搜索和机器学习方面的一系列应用。拟议的研究将探索最好的软件工具和平台方法，将新兴的QC能力整合到企业和研究工作流程中，通过简化和负担得起的将现实世界问题分解和制定成运行在量子处理器上的实现。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 broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project will to enable inexpensive access to quantum computing (QC) and to take the complexity out of the programming and application hosting tasks, which currently pose a major barrier to entry for potential users. QC technology is expected to disrupt significant portions of the high-performance computing environment for optimization problems, which has previously been characterized by slow and incremental performance improvements. This project would yield a platform that both increases the efficiency and lowers the cost of analyzing complex optimization problems, which could spur fast-paced innovation in wide areas of the economy that tackle such issues. This Small Business Innovation Research (SBIR) Phase II project addresses the need for a cloud-based platform for using QC technology. Early-generation quantum computers have been introduced by multiple hardware vendors. Despite advances in performance of QC processors, little effort has been directed toward developing programming environments and applications that can provide simple and inexpensive access to QC capabilities and that can exploit the power that QC systems will have in the near future. This project will develop a suite of front-end and back-end tools that efficiently transform high-level computing problems into formulations for circuit-model QC systems, abstracting away the physical low-level details and domain knowledge currently necessary to build QC applications. The project will further develop a set of applications in optimization, search, and machine learning. The proposed research will explore the best software tools and platform methods for integrating emerging QC capabilities into enterprise and research workflows by streamlining and making affordable the decomposition and formulation of real-world problems into implementations that run on quantum processors.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.