Performance of Quantum Approximate Optimization with Quantum Error Detection
Zichang He, David Amaro, Ruslan Shaydulin, Marco Pistoia

TL;DR
This paper demonstrates a partially fault-tolerant implementation of QAOA using the Iceberg error detection code, improving performance on a trapped-ion quantum computer and providing a model to predict future hardware capabilities.
Contribution
It introduces a partially fault-tolerant QAOA implementation with the Iceberg code and develops a predictive model for its performance on future quantum hardware.
Findings
Encoding with Iceberg code improves QAOA performance for up to 20 qubits.
The model predicts the limits of Iceberg code performance.
QAOA can outperform classical algorithms under certain hardware conditions.
Abstract
Quantum algorithms must be scaled up to tackle real-world applications. Doing so requires overcoming the noise present on today's hardware. The quantum approximate optimization algorithm (QAOA) is a promising candidate for scaling up, due to its modest resource requirements and documented asymptotic speedup over state-of-the-art classical algorithms for some problems. 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 ``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 logical qubits on a trapped-ion quantum computer. Additionally, we propose and calibrate a model for predicting the code…
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