Installation & first steps

Installation

To get started, install the QHyper library using pip. Open your terminal or command prompt and run the following command (make sure you are using Python 3.12):

pip install qhyper

Getting Started

1. Import an optimization problem:

Here, we will solve the Knapsack Problem, but check out the API for other available problems.

from QHyper.problems.knapsack import KnapsackProblem
problem = KnapsackProblem(max_weight=2,
                          item_weights=[1, 1, 1],
                          item_values=[2, 2, 1])

2. Create a solver:

Use the library’s classes to create a solver.

from QHyper.solvers.gate_based.pennylane import QAOA
from QHyper.optimizers import OptimizationParameter
from QHyper.optimizers.qml_gradient_descent import QmlGradientDescent

solver = QAOA(problem,
    layers=5,
    gamma=OptimizationParameter(init=[0.25, 0.25, 0.25, 0.25, 0.25]),
    beta=OptimizationParameter(init=[-0.5, -0.5, -0.5, -0.5, -0.5]),
    optimizer=QmlGradientDescent(),
    penalty_weights=[1, 2.5, 2.5],
)

3. Run experiments:

Run the solver.

solver_results = solver.solve()

4. Show the results:

Sort and display top 5 results.

from QHyper.util import sort_solver_results

sorted_results = sort_solver_results(
    solver_results.probabilities, limit_results=5)

print(sorted_results.dtype.names)
for result in sorted_results:
    print(result)

# ('x0', 'x1', 'x2', 'x3', 'x4', 'probability')
# (1, 1, 0, 0, 1, 0.24827694)
# (0, 1, 1, 0, 1, 0.18271937)
# (1, 0, 1, 0, 1, 0.18271937)
# (1, 1, 1, 0, 1, 0.15528488)
# (1, 1, 1, 1, 1, 0.03339847)

Summary

You have successfully installed the QHyper library and set up your first experiment.

Check out more advanced the tutorials: Solver configuration tutorial, Typical use cases, and Defining custom problems and optimizers.