Enabling Hybrid Algorithms on Utility-Era Devices via Error Suppression

While scalable and fault-tolerant quantum computers are currently out of reach, hybrid quantum-classical algorithms provide a path toward achieving quantum advantage for certain types of optimization problems on utility-era devices. Yet, errors and imperfections in existing quantum computers degrade the performance of these algorithms, rendering them unavailing. Various statistical techniques, such as ZNE and PEC, have been used to address this challenge. However, these methods can be applied to only a small subset of problems, and introduce an extensive sampling overhead, increasing the time and cost required to complete these tasks. We show that a deterministic error suppression workflow, with no overhead, improves the performance of hybrid algorithms on currently available quantum hardware by addressing both the quality of implementation of individual circuits and the effectiveness of the classical optimization loop. In this talk, we review the different parts of our workflow and demonstrate its effectiveness via the implementation of QAOA on real devices. We show orders of magnitude improvement in performance alongside a substantial reduction in the resources needed for achieving these tasks.

In particular, we show that we can consistently, with over 99% success rate, correctly solve combinatorial optimization problems, such as MaxCut and Min Vertex Cover, using minimal resources on >40Q superconducting quantum devices.