Getting Started#

What you can do#

Quantagonia’s HybridSolver is a cloud-based mathematical optimization solver that utilizes CPUs, GPUs, and QPUs. It allows to solve several classes of challenging optimization problems. Among others, you can tackle mixed-integer linear problems (MILPs) and quadratic unconstrained binary optimization (QUBO) problems. Depending on the settings, the solver uses classic and hybrid branch-and-cut and novel classic-quantum decomposition approaches.

The package can be used in two ways:

  1. A lightweight command-line interface (CLI) allows you to submit optimization tasks and track their progress from the command line.

  2. A Python API lets you call the HybridSolver directly from your Python codebase.

This guide covers the first steps to use both the CLI and the API of the HybridSolver.

Setup#

The HybridSolver CLI and API client are available as a Python package on PyPI. Note that Python >= 3.8, < 3.12 is required.

  1. Simply pip install the package, preferably inside a virtual environment:

    pip install quantagonia

Note

If you want to model QUBOs with the quantagonia package you have to install the extra qubo, i.e., by running pip install quantagonia[qubo].

  1. Set up authentication for the Quantagonia Cloud to be able to submit compute jobs to the cloud-based solver. If you don’t have an API key yet, sign up for a free plan at platform.quantagonia.com and store it in an environment variable:

    export QUANTAGONIA_API_KEY=<YOUR_API_KEY>
#for windows users: set QUANTAGONIA_API_KEY=<YOUR_API_KEY>

Command-Line Interface#

The command-line interface (CLI) allows you to submit and monitor optimization problems seamlessly and with minimal setup effort. If you followed the steps above, a hybridsolver binary is available in your path. The binary accepts several commands, e.g., to submit, monitor, or cancel an optimization task.

For a practical example, let’s say you have a mixed-integer linear optimization problem stored in MPS format in the file my_problem.mps. You can easily submit this problem to the cloud-based HybridSolver by making a simple call

hybridsolver submit my_problem.mps

which gives an output similar to:

✅ Submitted job with ID: 29870456-8837-4434-9e6b-1f68e38e2d7c

You can use the given ID to monitor the progress of the solver as follows:

hybridsolver monitor 29870456-8837-4434-9e6b-1f68e38e2d7c

This streams the solver’s progress to the console in real-time. If the log indicates that the problem has been solved successfully, the command

hybridsolver solution 29870456-8837-4434-9e6b-1f68e38e2d7c

retrieves and prints the optimal solution vector to the console. Note that there is also a solve command, which combines the submit and monitor commands and, hence, immediately streams the solver output after submitting the job.

Python API Client#

Instead of using the CLI, you can also call the HybridSolver within Python by importing the quantagonia package. This requires that you have followed the setup steps above. Then, set up a connection to the HybridSolver:

from quantagonia import HybridSolver, HybridSolverParameters

api_key = "QUANTAGONIA_API_KEY"
hybrid_solver = HybridSolver(api_key)

To solve an MPS file, setup solver parameters and use the solve method:

# choose input file
import os
input_file_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "data", "example.mps")

# setup parameters
params = HybridSolverParameters()
params.set_time_limit(180)

# solve problem
res_dict, _ = hybrid_solver.solve(input_file_path, params)

On successful execution, the process returns a dictionary containing the results:

# display information and solution values
print("Runtime:", res_dict["timing"])
print("Objective:", res_dict["objective"])
print("Bound:", res_dict["bound"])
print("Solution values:")
for idx, val in res_dict["solution"].items():
    print(f"\t{idx}: {val}")
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