# On Post-Processing the Results of Quantum Optimizers

## TL;DR

This paper analyzes the effectiveness of post-processing techniques, especially the Multi Qubit Correction algorithm, in improving quantum optimizer results, and compares it with built-in methods to guide future research and application choices.

## Contribution

The paper provides a detailed analysis of MQC's strengths and weaknesses and experimentally compares it with D-wave's built-in optimization, offering insights for better post-processing strategies.

## Key findings

- MQC often outperforms D-wave's built-in optimization.
- Pairing heuristics can influence MQC results.
- Built-in post-processing rarely surpasses MQC in tests.

## Abstract

The use of quantum computing for applications involving optimization has been regarded as one of the areas it may prove to be advantageous (against classical computation). To further improve the quality of the solutions, post-processing techniques are often used on the results of quantum optimization. One such recent approach is the Multi Qubit Correction (MQC) algorithm by Dorband. In this paper, we will discuss and analyze the strengths and weaknesses of this technique. Then based on our discussion, we perform an experiment on how pairing heuristics on the input of MQC can affect the results of a quantum optimizer and a comparison between MQC and the built-in optimization method that D-wave Systems offers. Among our results, we are able to show that the built-in post-processing rarely beats MQC in our tests. We hope that by using the ideas and insights presented in this paper, researchers and developers will be able to make a more informed decision on what kind of post-processing methods to use for their quantum optimization needs.