In this notebook, we briefly introduce the particularization of the Optimizer abstract plugin for scipy.optimize.minimize.
We assume that you are already familiar with the Optimize class. If not, you can access a detailed notebook introducting this class here.
ScipyMinimizePlugin is an Optimizer wrapping the scipy.optimize.minimize method, thus inheriting from all the underlying minimization algorithms.
The plugin can be instantiated as follows:
import numpy
from qat.vsolve.optimize import ScipyMinimizePlugin
from scipy.optimize import minimize
## A cobyla minimizer over any number of variables, random initialization, 20 max steps
cobyla = ScipyMinimizePlugin(tol=1e-2,
method="COBYLA",
options={"maxiter": 20})
Lets try to use this plugin to solve a QAOA instance.
from qat.vsolve.qaoa import MaxCut
import networkx as nx
import matplotlib.pyplot as plt
from qat.qpus import get_default_qpu
qpu = get_default_qpu()
stack = cobyla | qpu
graph = nx.cycle_graph(4)
problem = MaxCut(graph)
job = problem.to_job(2)
circuit = job.circuit
result = stack.submit(job)
print("The maxcut problem:")
print(problem)
print("Final energy:", result.value)
print("The optimization data:")
print(result.meta_data["optimizer_data"])
print("The best set of parameters:")
print(result.meta_data["parameters"])
Notice that the 'optimizer_data' entry of the result's meta_data contains the (stringified) output of scipy's minimize function.
As we can see, 20 iterations are not enough for the optimizer to converge. Lets try with 200:
cobyla = ScipyMinimizePlugin(method="COBYLA",
tol=1e-2,
options={"maxiter": 200})
stack = cobyla | qpu
result = stack.submit(job)
print("The maxcut problem:")
print(problem)
print("Final energy:", result.value)
print("The optimization data:")
print(result.meta_data["optimizer_data"])