EMQAOA-DARBO

Overview

This repository includes the codes and results for the manuscript: Error-mitigated Quantum Approximate Optimization via Learning-based Adaptive Optimization

Installation and usage

This repository requires to install two open-sourced packages:

• ODBO packge: The installation direction is provided in the corresponding main page.

• TencirCircuit or TC: pip install tensorcircuit

Content list

Files

• DARBO_optimization_ideal_example.ipynb: This is a simple example to illustrate the methods & to run a test MAX-CUT on a random graph with a circuit depth of 4.

• EMQAOA_DARBO_run.ipynb: This is the notebook to illustrate the EMQAOA-DARBO on the real hardware. This collects the hardwared data shown in the manuscript. Note: For non-Tencent-Quantum-Lab user, this set of codes cannot be run directly due to the unavailable access to the Tencent hardware. If you would like to have a try, please contact Tencent Quantum Lab to check the possible options for usage.

• si_more_stats.xlsx: This is a supplemental excel to summarize the optimized losses and $r$ values for different optimizers and different cases.

Folders

• codes: contains all the python codes that run the experiments collected in this work. (Please aware that all BO methods are formulated as a maximization problem (max -loss), and we save the -loss at each iteration. For other optimizers, we save loss at each iteration.)

• graph: contains the graphs used in this work.

• initialization: contains the presaved (& different) initialized parameters to make sure all different optimizers running from the same initial guesses.

• results: each subfolder contains the collected results for the corresponding

• plotting: contains a jupyter notebook to generate all the plots used in the paper. for_plotting folder contains the .txt summary for the results extracted from the raw results.

Please cite us as

@article{cheng2023darbo,
  title={Error-mitigated Quantum Approximate Optimization via Learning-based Adaptive Optimization},
  author={Cheng, Lixue and Chen, Yu-Qin and Zhang, Shi-Xin and Zhang, Shengyu},
  journal={arXiv preprint arXiv:2303.14877},
  year={2023}
}