NSF Award
1648832
The broader impact/commercial potential of this Small Business Technology Transfer (STTR) Phase I project will be to enable affordable access to quantum annealing quantum computers and to take the complexity out of the programming and application hosting tasks, which currently poses a major barrier of entry for potential users. The company expects quantum computing technology in the next few years 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. These sectors include energy distribution, pharmaceutical design, cancer research, data analytics, cybersecurity, autonomous systems, planning and scheduling activities, financial services such as risk management and portfolio optimization, and basic and applied research in physics and chemistry. In each of these disciplines, there are optimization-based computational problems that are currently intractable. The results of this research should enable a much larger community of experts to use the power of quantum computing to solve these important but currently intractable problems.<br/><br/>This Small Business Technology Transfer (STTR) Phase I project addresses the need for a cloud-based platform for using quantum annealing computing technology. Quantum annealing computers have come to market in the last few years, and research laboratories and universities have used these machines to explore algorithms that could eventually be solved efficiently on them. Despite advances in performance of quantum annealing computers, little effort has been directed toward developing programming environments and tools that provide simple and inexpensive access to quantum computing capabilities. This project researches a platform-as-a-service (PaaS) with a suite of front-end and back-end tools that efficiently transform high-level computing problems into binary optimization formulations suitable for quantum annealing, simplifying and automating the low-level details and domain knowledge currently necessary to perform useful calculations. This project will further develop the PaaS to include a classical-quantum computing environment and framework for analysis of large data sets using standard distributed computing tools. The research explores the best software tools and platform methods to integrate emerging quantum computing capabilities into workflows by streamlining and making affordable the processing of data and by decomposing real-world problems into sub-problems amenable to quantum computers of today and in the future.
