def test_trotterize_noiseless(self):
# Noiseless simulations
# Compare that the integrator adiabatic results match the trotterized results
# First, compare the non-IS subspace results
simulation = adiabatic_simulation(sample_graph(), IS_subspace=False)
res_trotterize = simulation.performance_vs_total_time(
np.arange(1, 5, 1),
metric='optimum_overlap',
initial_state=State(equal_superposition(6)),
schedule=lambda t, tf: simulation.linear_schedule(
t, tf, coefficients=[10, 10]),
plot=False,
verbose=True,
method='trotterize')
res_RK45 = simulation.performance_vs_total_time(
np.arange(1, 5, 1),
metric='optimum_overlap',
initial_state=State(equal_superposition(6)),
schedule=lambda t, tf: simulation.linear_schedule(
t, tf, coefficients=[10, 10]),
plot=False,
verbose=True,
method='RK45')
# Now test in the IS subspace
self.assertTrue(
np.allclose(res_trotterize[0]['trotterize']['optimum_overlap'],
res_RK45[0]['RK45']['optimum_overlap'],
atol=1e-2))
simulation = adiabatic_simulation(sample_graph(), IS_subspace=True)
res_trotterize = simulation.performance_vs_total_time(
np.arange(1, 4, 1),
metric='optimum_overlap',
schedule=lambda t, tf: simulation.linear_schedule(
t, tf, coefficients=[10, 10]),
plot=False,
verbose=True,
method='trotterize')
res_RK45 = simulation.performance_vs_total_time(
np.arange(1, 4, 1),
metric='optimum_overlap',
schedule=lambda t, tf: simulation.linear_schedule(
t, tf, coefficients=[10, 10]),
plot=False,
verbose=True,
method='RK45')
self.assertTrue(
np.allclose(res_trotterize[0]['trotterize']['optimum_overlap'],
res_RK45[0]['RK45']['optimum_overlap'],
atol=1e-2))
plt.scatter(depths, [i / (i + 1) for i in depths], label='maxcut')
plt.scatter(depths, mis, label='mis with $n=$' + str(n))
plt.plot(depths, mis)
plt.legend()
if show:
plt.show()
if __name__ == "__main__":
i, j = 4, 4
graph = node_defect_torus(i, j)
#simulation = adiabatic_simulation(graph, IS_subspace=True)
#t0 = time.time()
#res = simulation.performance_vs_total_time(np.arange(0, 0), metric='optimum_overlap',
# schedule=lambda t, tf: experiment_rydberg_MIS_schedule(t, tf, simulation,
# coefficients=[10,
# 10]),
# plot=True, verbose=True, method='trotterize')
#print('results: ', res, flush=True)
#simulation = mis_qaoa(4, IS_subspace=True, method='basinhopping')
simulation = adiabatic_simulation(graph, IS_subspace=False)
res = simulation.performance_vs_total_time(np.arange(40, 95, 5), metric='optimum_overlap',
initial_state=State(equal_superposition(i * j)),
schedule=lambda t, tf: simulation.linear_schedule(t, tf,
coefficients=[1, 1]),
plot=True, verbose=True, method='trotterize')
print('results: ', res, flush=True)
hb = hamiltonian.HamiltonianDriver()
hamiltonians = [hc, hb]
ring_hamiltonians = [hc_ring, hb]
sim = qaoa.SimulateQAOA(g, cost_hamiltonian=hc, hamiltonian=hamiltonians)
sim_ring = qaoa.SimulateQAOA(ring,
cost_hamiltonian=hc_ring,
hamiltonian=ring_hamiltonians)
sim_ket = qaoa.SimulateQAOA(g, cost_hamiltonian=hc, hamiltonian=hamiltonians)
sim_noisy = qaoa.SimulateQAOA(g,
cost_hamiltonian=hc,
hamiltonian=hamiltonians,
noise_model='channel')
# Initialize in |000000>
psi0 = State(equal_superposition(N))
rho0 = State(outer_product(psi0, psi0))
noises = [quantum_channels.DepolarizingChannel(rates=(.001, ))]
sim_noisy.noise = noises * 2
class TestSimulateQAOA(unittest.TestCase):
def test_variational_grad(self):
# Test that the calculated objective function and gradients are correct
# p = 1
sim_ket.hamiltonian = hamiltonians
F, Fgrad = sim_ket.variational_grad(np.array([1, 0.5]),
initial_state=psi0)
self.assertTrue(np.abs(F - 5.066062984904652) <= 1e-5)
self.assertTrue(