Today's high-performance computers are limited in solving problems with large state spaces. We propose accelerating solutions to the Air Force's machine learning, combinatorial optimization, and advanced micro- and macro-simulation problems. This work leverages universal gate-based quantum processors, based on superconducting quantum bits (qubits), deployed in a hybrid cloud computing architecture. Specifically, we will provide a license to access Rigetti's quantum-first computing capabilities. We will augment this access with support to explore quantum-accelerable candidate problems and to develop a low-qubit proof-of-concept for one high-value problem. Our work will demonstrate the value of quantum computing in solving problems better, faster, or cheaper than classical computers in the era of noisy, intermediate-scale quantum (NISQ) computers. For the Air Force, the results will create a path to building cutting-edge computing capabilities and a quantum-ready workforce. The work also contributes to building computing capability that is expected to serve as the infrastructure for the US economy for applications such as discovering new medicines, creating new materials, optimizing asset and risk portfolios, leveraging big data, and efficient logistics.
