Parameter-Dependent Noise Resilience in the Quantum Approximate Optimization Algorithm

@misc{kirk2026parameterdependent,
  title = {{Parameter-Dependent Noise Resilience in the Quantum Approximate Optimization Algorithm}},
  author = {Jorja Kirk and Ashley Montanaro},
  howpublished = {arXiv},
  year = {2026},
  month = oct,
  doi = {10.48550/arxiv.2610.11710},
  eprint = {2610.11710},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2610.11710},
}

TY  - UNPB
TI  - Parameter-Dependent Noise Resilience in the Quantum Approximate Optimization Algorithm
AU  - Jorja Kirk
AU  - Ashley Montanaro
PY  - 2026
DA  - 2026/10/08
PB  - arXiv
DO  - 10.48550/arxiv.2610.11710
UR  - https://arxiv.org/abs/2610.11710
AB  - The Quantum Approximate Optimization Algorithm (QAOA) is a variational quantum algorithm used for solving combinatorial optimization problems, which is thought to be a promising application of near-term quantum hardware. However, high rates of noise in these devices can obscure meaningful results. Therefore, it is crucial to understand and suppress the effects of noise on the algorithm's output. Here, we find that the stability of the algorithm in the presence of noise is dependent on its variational parameters. Using a Pauli noise model, we derive an upper bound on the total variation distance between the noisy and noiseless circuit output as a function of these parameters. Results from noisy simulations support this bound and give insight into the best methods to use for parameter generation in the presence of noise. We also introduce new methods of optimization using error penalties and truncated search based on this bound to manufacture error resilience. We extend this analysis to the quantum adiabatic algorithm (QAA) by taking the continuous limit of the QAOA.
ER  -