GitHub - Q2TM/qubo_benchmarking: This project benchmarks QUBO problems using classical solvers, gate-based quantum computing, and D-Wave's quantum annealing. Focusing on TSP, 3-SAT, and QAP, it evaluates performance, accuracy, and scalability.

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This project benchmarks QUBO problems using classical solvers, gate-based quantum computing, and D-Wave's quantum annealing. Focusing on TSP, 3-SAT, and QAP, it evaluates performance, accuracy, and scalability.

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Name		Name	Last commit message	Last commit date
Latest commit History 80 Commits
3SAT		3SAT
QAP		QAP
TSP		TSP
Utils		Utils
.env.example		.env.example
.gitignore		.gitignore
D-Wave Hamiltonian matrix.py		D-Wave Hamiltonian matrix.py
General Transverse Field Ising Hamiltonian matrix.py		General Transverse Field Ising Hamiltonian matrix.py
README.txt		README.txt
Transverse Field Ising Hamiltonian Matrix.py		Transverse Field Ising Hamiltonian Matrix.py
paper.tex		paper.tex
poetry.lock		poetry.lock
pyproject.toml		pyproject.toml

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## Important

These solver clients require sign ups and tokens to use:
1. Fixstar 
2. D-Wave

*Gurobi requires an application to be downloaded to your local machine.

Py libraries required for D-wave:
1. dwave-system
2. minorminer

QUBO problems covered:
1. Quadratic Assignment problem
2. Travelling Salesman problem
3. 3-satisfiability problem

## Steps to run solvers:
1. Run $ cd Utils
2. Run $ cp .env.sample .env
3. Paste your tokens into the newly created file
4. Run solvers.ipynb on jupyter or jupyter labs

Benchmark criteria:
1. Optimal solutions
2. Time
3. Code complexity
4. Cost