Welcome to the QPLEX documentation¶
Contents:
Introduction¶
QPLEX is an open-source Python library that enables developers to implement combinatorial optimization models and execute them seamlessly on multiple classical and quantum devices (using different quantum algorithms).
Our solution automatically handles the adaptation of the optimization model to the specific instructions of the target quantum device’s SDK.
The library supports unconstrained and constrained problems, as well as binary, discrete, and continuous variables. QPLEX uses DOcplex as a modeling API allowing the creation of optimization models using the exact same syntax as this base library.
Installation¶
pip install your-library
Quick Start¶
A brief example of how to use your library:
from qplex import QModel
from qplex.model import Options
# Problem definition
weights = [4, 2, 5, 4, 5, 1, 3, 5]
values = [10, 5, 18, 12, 15, 1, 2, 8]
max_weight = 15
n = len(values)
# Build the model
knapsack_model = QModel('knapsack')
x = knapsack_model.binary_var_list(n, name="x")
knapsack_model.add_constraint(
sum(weights[i] * x[i] for i in range(n)
) <= max_weight)
obj_fn = sum(values[i] * x[i] for i in range(n))
knapsack_model.set_objective('max', obj_fn)
execution_params = {
"provider": "braket",
"backend": "simulator",
"algorithm": "qaoa",
"p": 4,
"max_iter": 500,
"shots": 10000
}
knapsack_model.solve(solver='quantum', Options(**execution_params))
