pip install qubots

mkdir my_optimizer && cd my_optimizer

from qubots import BaseOptimizer

class MyOptimizer(BaseOptimizer):
    def optimize(self, problem):
        # Your optimization logic here
        solution = self.solve_problem(problem)
        return self.create_result(solution)

    def solve_problem(self, problem):
        # Implement your algorithm
        return problem.get_random_solution()

{
  "type": "optimizer",
  "entry_point": "qubot",
  "class_name": "MyOptimizer",
  "default_params": {
    "time_limit": 60.0
  },
  "metadata": {
    "name": "My Optimizer",
    "description": "Custom optimization algorithm"
  }
}

from qubots import AutoOptimizer, AutoProblem

# Load your optimizer
optimizer = AutoOptimizer.from_repo(".")

# Test with example problem
problem = AutoProblem.from_repo("examples/maxcut_problem")
result = optimizer.optimize(problem)
print(f"Result: {result.best_value}")

# Install upload tool
pip install qubots[upload]

# Upload to platform
qubots upload . --name "my_optimizer" --token YOUR_TOKEN

import qubots.rastion as rastion

# Authenticate
rastion.authenticate("YOUR_TOKEN")

# Upload optimizer
rastion.upload_optimizer(
    path=".",
    name="my_optimizer",
    description="My custom optimizer",
    visibility="public"  # or "private"
)

# Load from platform
optimizer = rastion.load_qubots_model("my_optimizer", username="your_username")

# Run in cloud
from qubots import execute_playground_optimization

result = execute_playground_optimization(
    problem_name="maxcut_problem",
    problem_username="examples",
    optimizer_name="my_optimizer",
    optimizer_username="your_username"
)

from qubots import BenchmarkSuite

suite = BenchmarkSuite("My Comparison")
suite.add_problem(problem, "test_case")
suite.add_optimizer(optimizer, "my_algo")
results = suite.run_full_benchmark(num_runs=5)

# Override default parameters
optimizer = AutoOptimizer.from_repo(".", override_params={
    "time_limit": 120.0,
    "population_size": 200
})