def maxcut_qaoa(n_step, edges, minimizer=None, sampler=None, verbose=True):
"""Setup QAOA.
:param n_step: The number of step of QAOA
:param n_sample: The number of sampling time of each measurement in VQE.
If None, use calculated ideal value.
:param edges: The edges list of the graph.
:returns Vqe object
"""
sampler = sampler or vqe.non_sampling_sampler
minimizer = minimizer or vqe.get_scipy_minimizer(
method="Powell",
options={"ftol": 5.0e-2, "xtol": 5.0e-2, "maxiter": 1000, "disp": True}
)
hamiltonian = pauli.I() * 0
for i, j in edges:
hamiltonian += pauli.Z(i) * pauli.Z(j)
return vqe.Vqe(vqe.QaoaAnsatz(hamiltonian, n_step), minimizer, sampler)
def numpartition_qaoa(n_step, nums, minimizer=None, sampler=None):
"""Do the Number partition QAOA.
:param n_step: The number of step of QAOA
:param nums: The edges list of the graph.
:returns Vqe object
"""
hamiltonian = pauli.Expr.zero()
for i, x in enumerate(nums):
hamiltonian += pauli.Z[i] * x
hamiltonian = (hamiltonian**2).simplify()
return vqe.Vqe(vqe.QaoaAnsatz(hamiltonian, n_step), minimizer, sampler)
if __name__ == "__main__":
minimizer = vqe.get_scipy_minimizer(method="Powell",
options={"disp": True})
nums = [3, 2, 6, 9, 2, 5, 7, 3, 3, 6, 7, 3]
runner = numpartition_qaoa(2, nums, minimizer=minimizer)
vqeresult = runner.run(verbose=True)
print("Num partition:", nums)
best = vqeresult.most_common()[0]
print("Probability:", best[1])
result = "".join(map(str, best[0]))
group0 = [a for a, b in zip(nums, result) if b == '0']
group1 = [a for a, b in zip(nums, result) if b == '1']
print("Group 0:", sum(group0), group0)
print("Group 1:", sum(group1), group1)