Algorithms for Quantum Utility: Intelligent, Robust, and Efficiently Distributed (AQUIRED)

Quantum computing is a new, potentially very powerful type of computing. Great interest has been generated in recent years by a plethora of proof-of-principle demonstrations on small prototype devices. However, such demonstrations have employed methods that are unlikely to work at scales of practical interest. We propose three lines of research to maximize the utility of quantum computers as they scale from small devices to large distributed systems: (1) We will develop and demonstrate scalable hybrid quantum-classical algorithms for combinatorial optimization by designing problem-tailored ansatzes and advancing novel methods of incorporating them into classical workflows. (2) We will develop and demonstrate kernels for quantum simulation that exploit mid-circuit measurements for improved efficiency and robustness to hardware errors. (3)We will develop models and algorithms for future distributed quantum computing architectures, for which parallelism and communication complexity are as important as computational complexity. This work directly addresses the call to 'advance the research towards achievement and demonstration of quantum utility by developing new algorithms.' It does so by developing algorithms for DOE application inspired computational kernels for both near-term and future quantum systems and by estimating quantum resources for those algorithms. Significantly, this work will deliver near-term successes for quantum computing while the technology matures, justifying continued Department of Energy (DOE) investments in quantum computing. Equally significant, this project will perform necessary groundwork to utilize larger, distributed quantum computers when they become available. These efforts will help DOE leverage quantum computing as a useful scientific tool as early as possible, with potential benefits to diverse applications such as the discovery of materials and processes for clean energy and the design and optimization of resilient distributed energy networks.