Benchmark and Simulation Data for XQAOA

This repository includes the $D$-regular graphs and the optimal cut solutions that were utilized to evaluate the performance of both classical and quantum algorithms in the research paper titled An Expressive Ansatz for Low-Depth Quantum Optimization. The repository also comprises the simulation data and scripts utilized to generate the plots in the paper. Additionally, it includes a minimal working example in Python that demonstrates how to simulate the XQAOA ansatz using classical methods. Our goal is to make this repository useful for those who want to compare the performance of their quantum algorithms against the MaxCut problem.

Benchmark Data

In the benchmark_data folder, you'll find a total of $420$ $D$-regular graphs. This includes $20$ $3$-regular graphs, each with 16 vertices, and $200$ $D$-regular graphs of $128$ and $256$ vertices for $3 \leq D \leq 10$. These graphs are presented in a machine-readable CSV format and are named according to the convention G{D}#{N}_{I}.csv, where $D$ stands for the degree of the graph, $N$ represents the number of vertices, and $I$ represents the specific instance of the graph. For each combination of $D$ and $N$ (except for $N = 16$), there are $25$ unique instances.

File Structure

The file structure of each G{D}#{N}_{I}.csv file are as follows: - The first line contains four comma separated values. They are - $N$ - The number of vertices in the graph. - $M$ - The number of edges in the graph. - $C{opt}$ - The cost of the optimal cut of the graph. - MIPGap - The gap between the lower and upper objective bound divided by the absolute value of the incumbent objective value. A MIPGap of $0.0$ indicates that the solution is optimal. - The second line contains the assignments that give the optimal cut $C{opt}$. The $\pm 1$ value at index $i$ corresponds to the value assigned to the node $i$ for the nodes in the range $0, \dots, N-1$. - The lines $3$ till $M+2$ contain the edges that generate the $D$-regular graph.

Usage

The following Python code allows to reads the G{D}#{N}_{I}.csv and returns the graph attributes along with the list of edges. The edge list can be used to construct the $D$-regular graph using libraries such as the NetworkX library. ```python import csv

def readgraph(D, N, I): edges = [] with open(f"benchmarkdata/G{D}#{N}{I}.csv") as graphfile: graphreader = csv.reader(graphfile, delimiter=',') for i, row in enumerate(graphreader): if i == 0: _, M, Copt, MIPGap = row elif i == 1: solution = row else: i, j = row edges.append((int(i), int(j))) solution = [int(x) for x in solution] graphattr = {"Nodes": N, "Edges": int(M), "Cost": float(Copt), "MIPGap": float(MIPGap), "Solution": solution} return graph_attr, edges ```

Simulation Data

The data collected in the numerical simulations are located in the simulation_data folder. They too are present in a machine-readable CSV format, however, these CSV files are also readable by the Python Pandas library (Pandas can read these CSV files as a DataFrame object.) The CSV file names follow the same convention as those in benchmark_data folder, G{D}#{N}_{I}.csv, where $D$ stands for the degree of the graph, $N$ represents the number of vertices, and $I$ represents the specific instance of the graph.

The CSV files present in the root of the simulation_data folder contain the numerical simulation results of benchmarking the $p = 1$ ansatz for the XQAOA (X=Y Mixer), MA-QAOA, QAOA, and QAOA* against the Classical-Relaxed and the state-of-the-art Goemans-Williamson algorithm for $100$ runs for all the graphs present in the benchmark_data folder. The CSV files present in the following folders - simulationdata/XQAOANo_Gamma - simulationdata/XYQAOA - simulationdata/YQAOA

contain the numerical data for the $p = 1$ ansatz for the XQAOA (X=Y Mixer with $\gamma = 0$), XQAOA (XY Mixer), and the XQAOA (Y Mixer) on the first $10$ instances of the G3#128_{I}.csv graphs.

The CSV files present in simulationdata/FIG5Dataset and simulationdata/FIG7Dataset contain the data to generate the plots in Figures 5 and 7 of the paper. Please note that these CSV files (with the exception of simulationdata/FIG7Dataset) are not readable by the Pandas as a DataFrame object.

For usage of these simulation data, please refer the python scripts present in the plot_scripts folder.

Minimal Working Example

The mwe folder contains the minimal working example implemented in Python for classically simulating the XQAOA ansatz on the MaxCut problem. The minimal working example consists of three files - Graph.py - This script stores a graph along with its relevant attributes in a datastructure that is most suited to the needs of the XQAOA ansatz. - XQAOA.py - This scripts contains the XQAOA class that allows for classical simulation of the XQAOA ansatz for the MaxCut problem. - Example.ipynb - A Jupyter Notebook that contains an example for classically simulating the XQAOA ansatz and its variants on a simple $3$-regular graph.

Usage and Citation

Please consider citing this repository and our paper An Expressive Ansatz for Low-Depth Quantum Optimization if you find this repository useful and use it in your research for benchmarking purposes.