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Quantagonia

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

Solvers​

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

Quadratic Unconstrained Binary Optimization (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)

Mixed Integer Linear Programming (MILP)​

Quantagonia is only able to solve MPS problems of the MILP variety.

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​

About presolving

You'll need access to the Gurobi product, if you want to 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.

Linear Programming (LP)​

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(.....)

Solvers

  • quantagonia.qubo
  • quantagonia.mps
ParameterTypeDescriptionDefaultValues
sensestringDetermines the optimization direction of the problem, either maximizing or minimizing the objective function.MINIMIZEMAXIMIZE, MINIMIZE
timelimitfloatSets the maximum time, in seconds, that the solver should spend on the problem before terminating.86400-
relative_gapfloatSpecifies the stopping criterion based on the relative gap between the best found solution and the bound.0.0001-
absolute_gapfloatSets 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_onlybool(Only for QUBO) If set to true, the solver will only use heuristic methods to find a solution, potentially faster but possibly less optimal.falsetrue, false