Dataset for Comparing Three Generations of D-Wave Quantum Annealers for Minor Embedded Combinatorial Optimization Problems

Citation Author(s):
Elijah
Pelofske
Los Alamos National Laboratory
Submitted by:
Elijah Pelofske
Last updated:
Fri, 01/13/2023 - 19:12
DOI:
10.21227/39xb-sa24
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Abstract 

Dataset for the paper titled "Comparing Three Generations of D-Wave Quantum Annealers for Minor Embedded Combinatorial Optimization Problems". This data includes the minor embeddings and raw D-Wave measurements.

Instructions: 

# Random graph instances

These graphs can be read in using the python module networkx

```
import networkx as nx
import ast

for problem_index in range(200):
file = open("fixed_graphs/graph_edges_"+str(problem_index)+".txt", "r")
edges = ast.literal_eval(file.read())
file.close()
G = nx.Graph(edges)
```

# Minor embeddings

The minor embeddings are python dictionaries, which can be read in using the following code:
```
import ast
file = open("minor_embeddings/Advantage2_prototype1.1_52.txt", "r")
embedding = ast.literal_eval(file.read())
file.close()
```

# D-Wave results

The raw D-Wave measurement results are in the directories `DWave_results_Maximum_Clique` and `DWave_results_Maximum_Cut`

These directories also contain timing files in seconds for each experiment.

The DWave results are all compressed zip files:

```
import json
import zlib

with open("DWave_results_Maximum_Cut/Max_Cut_144_8_10_Advantage_system4.1_solutions.json.zip", mode="rb") as fin:
data = fin.read()
decompressed = zlib.decompress(data)
data = json.loads(decompressed)
```

The format of these datafiles is "Max_Cut_PROB_CS_AT_device" (or "Max_Clique_PROB_CS_AT_device"), where PROB is a unique index for the problem, CS is chain strength, and AT is annealing time.