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/商业潜力将使量子计算（QC）的成本低廉，并消除编程和应用程序托管任务的复杂性，目前这对潜在用户的进入构成了主要障碍。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，这些问题以前的特点是缓慢和增量的性能改进。该项目将产生一个平台，既提高了效率，又降低了分析复杂优化问题的成本，这可能会刺激解决此类问题的广泛经济领域的快节奏创新。这个小型企业创新研究（SBIR）第二阶段项目解决了使用QC技术的云平台的需求。早期的量子计算机已经由多家硬件供应商推出。尽管在QC处理器的性能方面取得了进步，但很少有人致力于开发能够提供简单且廉价的QC能力访问并且能够利用QC系统在不久的将来将具有的能力的编程环境和应用。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程序。拟议的研究将探索最好的软件工具和平台方法，通过简化和负担得起的分解和制定真实的，将新兴的QC能力整合到企业和研究工作流程中。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进行评估，被认为值得支持的搜索.

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.