Performance of quantum approximate optimization with quantum error detection
Zichang He, David Amaro, Ruslan Shaydulin, Marco Pistoia
Abstract
The quantum approximate optimization algorithm (QAOA) is a promising candidate for scaling up to tackle real-world applications. However, achieving better-than-classical performance with QAOA is believed to require fault tolerance. In this paper, we demonstrate a partially fault-tolerant implementation of QAOA using the [[k + 2, k, 2]] “Iceberg” error detection code. We observe that encoding the circuit with the Iceberg code improves the algorithmic performance as compared to the unencoded circuit for problems with up to 20 logical qubits on a trapped-ion quantum computer. Additionally, we propose and calibrate a model for predicting the code performance. We use this model to characterize the limits of the Iceberg code and extrapolate its performance to future hardware with improved error rates. To the best of our knowledge, our results demonstrate the largest universal quantum computing algorithm protected by partially fault-tolerant quantum error detection on practical applications to date. The authors present a partially fault-tolerant implementation of the quantum approximate optimization algorithm. By encoding circuits with the iceberg error detection code, the authors improve QAOA’s performance on problems with up to 20 logical qubits on a trapped-ion quantum computer, outlining conditions required to surpass classical algorithms.