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Example: generic tensor network #22

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Example: generic tensor network #22

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2 changes: 1 addition & 1 deletion .gitignore
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*.jl.*.cov
*.jl.cov
*.jl.mem
/Manifest.toml
Manifest.toml
lib
.vscode
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13 changes: 13 additions & 0 deletions examples/combinatorial-optimization/Project.toml
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[deps]
CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"
CuTropicalGEMM = "c2b282c3-c9c2-431d-80f7-a1a0561ebe55"
GenericTensorNetworks = "3521c873-ad32-4bb4-b63d-f4f178f42b49"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
TropicalNumbers = "b3a74e9c-7526-4576-a4eb-79c0d4c32334"

[compat]
CUDA = "5"
CuTropicalGEMM = "0.1"
GenericTensorNetworks = "1"
TropicalNumbers = "0.6"
julia = "1.9"
8 changes: 8 additions & 0 deletions examples/combinatorial-optimization/README.md
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# Solving combinatorial optimization problem with generic tensor networks

## Problem description

We consider solving the maximum independent set problem. Please check [the manual page](https://queracomputing.github.io/GenericTensorNetworks.jl/dev/generated/IndependentSet/) of package [GenericTensorNetworks.jl](https://github.com/QuEraComputing/GenericTensorNetworks.jl) [1] for a detailed description of the problem.

## References
1. Liu, J.-G., Gao, X., Cain, M., Lukin, M. D. & Wang, S.-T. Computing solution space properties of combinatorial optimization problems via generic tensor networks. Preprint at https://doi.org/10.48550/arXiv.2205.03718 (2022).
22 changes: 22 additions & 0 deletions examples/combinatorial-optimization/main.jl
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using GenericTensorNetworks, CUDA, TropicalNumbers, GenericTensorNetworks.Graphs
using CuTropicalGEMM
using Random; Random.seed!(2)

# Create a random 3-regular graph
g = GenericTensorNetworks.random_diagonal_coupled_graph(38, 38, 0.8)

# Create a tensor network representation for the independent set problem on this graph
# Let us specify the tensor network contraction order optimizer to be TreeSA, which is a local search algorithm
tn = IndependentSet(g; optimizer=TreeSA(ntrials=1, niters=5))

# Let us check its contraction complexity
contraction_complexity(tn)

# Let us find the maximum independent set using the tensor network contraction.
# It will use the CuTropicalGEMM library to perform the contraction.
# Please use Float32 type for the best performance.
@time Array(solve(tn, SizeMax(); usecuda=true, T=Float32))
# output: 1.1s

# If you want to use the automatic differentiation based approach to find the optimal solution.
@time solve(tn, SingleConfigMax(; bounded=true); usecuda=true, T=Float32)