using JuMP
using QUBODrivers
model = Model(RandomSampler.Optimizer)
Q = [
-1.0 2.0 2.0
2.0 -1.0 2.0
2.0 2.0 -1.0
]
@variable(model, x[1:3], Bin)
@objective(model, Min, x' * Q * x)
optimize!(model)
for i = 1:result_count(model)
xi = value.(x; result=i) # Solution vector
yi = objective_value(model; result=i) # Energy
println("f($xi) = $(yi)")
end
f([0.0, 0.0, 1.0]) = -1.0
f([1.0, 0.0, 0.0]) = -1.0
f([0.0, 1.0, 0.0]) = -1.0
f([0.0, 0.0, 0.0]) = 0.0
f([1.0, 0.0, 1.0]) = 2.0
f([1.0, 1.0, 0.0]) = 2.0
f([0.0, 1.0, 1.0]) = 2.0
f([1.0, 1.0, 1.0]) = 9.0