Quantagonia

Quantagonia offers a Hybrid Quantum Platform for solving complex computational problems.

Solvers

Model	Backend
BinaryQuadraticModel	quantagonia.qubo
MPSFile	quantagonia.mps

Binary Quadratic Model

import strangeworks as sw
from strangeworks_optimization import StrangeworksOptimizer
from strangeworks_optimization_models.parameter_models import QuantagoniaParameterModel
from dimod import BinaryQuadraticModel

sw.authenticate('your-api-key')

linear = {1: -2, 2: -2, 3: -3, 4: -3, 5: -2}
quadratic = {(1, 2): 2, (1, 3): 2, (2, 4): 2, (3, 4): 2, (3, 5): 2, (4, 5): 2}
model = BinaryQuadraticModel(linear, quadratic, "BINARY")

options = QuantagoniaParameterModel(sense="MINIMIZE")

solver = "quantagonia.qubo"

so = StrangeworksOptimizer(
    model=model, 
    solver=solver, 
    options=options)
sw_job = so.run()

print(f"Job slug: {sw_job.slug}")
print(f"Job status: {so.status(sw_job.slug)}")

results = so.results(sw_job.slug)

Mathematical Programming System

import strangeworks as sw
from strangeworks_optimization import StrangeworksOptimizer
from strangeworks_optimization_models.problem_models import MPSFile
from strangeworks_optimization_models.parameter_models import QuantagoniaParameterModel
from dimod import BinaryQuadraticModel

sw.authenticate('your-api-key')

file = "example.mps"
model = MPSFile.read_file(file)

options = QuantagoniaParameterModel(sense="MINIMIZE")

solver = "quantagonia.mps"

so = StrangeworksOptimizer(
    model=model, 
    solver=solver, 
    options=options)
sw_job = so.run()

print(f"Job slug: {sw_job.slug}")
print(f"Job status: {so.status(sw_job.slug)}")

results = so.results(sw_job.slug)

QuantagoniaParameterModel

Parameter	Type	Description	Default	Values
sense	string	Determines the optimization direction of the problem, either maximizing or minimizing the objective function.	MINIMIZE	MAXIMIZE, MINIMIZE
timelimit	float	Sets the maximum time, in seconds, that the solver should spend on the problem before terminating.	86400	-
relative_gap	float	Specifies the stopping criterion based on the relative gap between the best found solution and the bound.	0.0001	-
absolute_gap	float	Sets the absolute gap tolerance, which is the acceptable difference between the best solution's objective value and the solver's bound for concluding optimization.	0.000000001	-
heuristics_only	bool	(Only for QUBO) If set to true, the solver will only use heuristic methods to find a solution, potentially faster but possibly less optimal.	False	True, False

For further details on these parameters and more in-depth explanations, you can consult the Quantagonia documentation directly at Quantagonia's API Documentation.