Quantum algorithm co-design for field-testing the path to advantage

To help end users to appreciate both the current status and the potential future impact of quantum computing, providing practical-scale solutions that leverage quantum resources is a powerful demonstration. These demonstrations ideally satisfy three criteria: (1) a current quantum computation at non-trivial scale, (2) a smooth scaling approach such that the quantum computation can measurably scale towards a target threshold for potential quantum advantage, and (3) algorithm design that minimizes the most challenging resources for a given hardware. We illustrate this approach using a scheduling and routing scenario for the Australian Army: our hybrid quantum algorithm provides solutions for full-scale use-cases and features (1) a current quantum routing sub-routine for ~6 vehicles on several available routes, (2) a problem decomposition method that allows scaling of the number of vehicles towards the classically-challenging threshold at ~100 vehicles, and (3) resource requirements of only sparse two-qubit connectivity outside local qubit clusters and circuit duration less than 1ms for classically-challenging problems (within challenging but achievable T1 limits) on superconducting devices. We highlight the impact of Q-CTRL’s error-reducing infrastructure software, which increases current high-quality solution size (2X larger than direct hardware deployment), provides >6X improvement in time-to-solution, and reduces the device T1 limit required for large scale problems by ~20%.