Eliminating overhead: Improving the performance of hybrid algorithms using deterministic error suppression

Large-scale fault-tolerant quantum computers will enable new solutions for problems known to be hard for classical computers. While scalable and fault-tolerant quantum computers are currently out of reach, hybrid quantum-classical algorithms provide a path towards achieving quantum advantage for certain types of optimization problems on NISQ devices. Errors and imperfections in existing quantum computers degrade the performance of these algorithms. Various statistical techniques have been used to address this issue, including zero noise extrapolation and random compilation methods such as Pauli twirling. These techniques introduce additional sampling overhead, increasing the hardware execution time required to complete the tasks. In this talk, we show that a deterministic error suppression workflow improves the performance of hybrid algorithms on currently available quantum hardware. This is done by improving the performance of arbitrary quantum circuits on the hardware, without introducing any additional overhead. We demonstrate the effectiveness of our tools via the implementation of QAOA and VQE on real devices. Our workflow improves the structural similarity index (SSIM) of the energy landscape (compared to the ideal landscape) by 28x for a 5-qubit QAOA problem, and improves the mean energy deviation (from the ideal energy) by 5x for a 6-qubit VQE problem. In this way, our methods render non-working algorithms into useful ones, while keeping the required hardware time low.