Quantagonia

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

Solvers

Model	Solvers
Quadratic Unconstrained Binary Optimization (QUBO)	quantagonia.qubo
Mixed Integer Linear Programming (MILP)	quantagonia.mps
Linear Programming (LP)	quantagonia.lp

QUBO

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

sw.authenticate(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)

MILP

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(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)

Presolve

If you have access to the Gurobi product, you can use the gurobi.presolve service to preprocess the problem before submitting to Quantagonia. This can often lead to a significant reduction in the size of the problem and make the solver more efficient.

Read more about presolving MPS problems

Linear Programming

from strangeworks_optimization import StrangeworksOptimizer
from strangeworks_optimization_models.parameter_models import QuantagoniaParameterModel
from strangeworks_optimization_models.problem_models import LpFile

model = LpFile.read_file("example.lp")
options = QuantagoniaParameterModel(sense="MINIMIZE")

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

results = so.results(sw_job.slug)

Parameters

from strangeworks_optimization_models.parameter_models import QuantagoniaParameterModel

options = 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.	1e-9	-
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

Solvers

• quantagonia.qubo
• quantagonia.mps

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