Maximizing success and minimizing resources: An optimal design of hybrid algorithms for NISQ era devices

Hybrid algorithms can potentially deliver quantum advantage in problems from physics, chemistry and optimization. Practical implementations on current available quantum hardware are still challenged by optimization inefficiency, poor scalability, runtime overhead and inaccuracy. In this work, we redesign the QAOA and VQE algorithm systematically to efficiently improve the success rate and accuracy while using minimal resources. By automating and optimising the ansatz selection, cost function and parameters optimization we can reduce the complexity of the classical component of hybrid algorithms, leading to a much faster, stable and consistent convergence. We show the redesigned QAOA procedure exhibiting 6x improvement in success probability solving Max-Cut problem with at least 3 times less function calls compared to commonly used randomised initialization methods. We also demonstrate an improved accuracy and convergence speed from the redesigned VQE procedure. Finally, we demonstrate further algorithmic improvements achieved by applying our deterministic error-suppression workflow on NISQ hardware, which provides the hybrid algorithm a robust noise resistance and enables the scalability to larger devices.