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test_two_qubits.py
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"""
integration testing module for C1 optimization through two-qubits example
"""
import pickle
import numpy as np
import pytest
from numpy.testing import assert_array_almost_equal as almost_equal
import c3.libraries.algorithms as algorithms
from c3.libraries.fidelities import state_transfer_from_states
# Libs and helpers
from c3.libraries.propagation import rk4_unitary
import tensorflow as tf
with open("test/two_qubit_data.pickle", "rb") as filename:
test_data = pickle.load(filename)
@pytest.mark.tensorflow
@pytest.mark.unit
def test_signals(get_two_qubit_chip) -> None:
"""Compare generated control signals to stored examples."""
exp = get_two_qubit_chip
pmap = exp.pmap
gen_signal = pmap.generator.generate_signals(pmap.instructions["rx90p[0]"])
ts = gen_signal["d1"]["ts"]
np.testing.assert_allclose(ts, test_data["ts"])
np.testing.assert_allclose(
actual=gen_signal["d1"]["values"].numpy(),
desired=test_data["signal"]["d1"]["values"].numpy(),
)
@pytest.mark.unit
def test_hamiltonians(get_two_qubit_chip) -> None:
"""Compare Hamilonians"""
model = get_two_qubit_chip.pmap.model
hdrift, hks = model.get_Hamiltonians()
assert (hdrift.numpy() - test_data["hdrift"].numpy() < 1).any()
for key in hks:
almost_equal(hks[key], test_data["hks"][key])
@pytest.mark.tensorflow
@pytest.mark.integration
def test_propagation(get_two_qubit_chip) -> None:
"""Test that result of the propagation code does not change."""
GATE_STR = "rx90p[0]"
exp = get_two_qubit_chip
pmap = exp.pmap
instr = pmap.instructions[GATE_STR]
steps = int((instr.t_end - instr.t_start) * exp.sim_res)
result = exp.propagation(
pmap.model,
pmap.generator,
pmap.instructions["rx90p[0]"],
exp.folding_stack[steps],
)
propagator = result["U"]
almost_equal(propagator, test_data["propagator"])
@pytest.mark.unit
def test_init_point(get_OC_optimizer) -> None:
"""Check that a previous best point can be loaded as an initial point."""
opt = get_OC_optimizer
opt.load_best("test/best_point_open_loop.c3log")
@pytest.mark.slow
@pytest.mark.tensorflow
@pytest.mark.optimizers
@pytest.mark.integration
def test_optim(get_OC_optimizer) -> None:
"""
check if optimization result is below 1e-1
"""
opt = get_OC_optimizer
assert opt.evaluation == 0
opt.optimize_controls()
assert opt.current_best_goal < 0.1
maxiterKey = "maxiters" if opt.algorithm == algorithms.tf_sgd else "maxiter"
assert opt.evaluation == opt.options[maxiterKey] - 1
@pytest.mark.slow
@pytest.mark.tensorflow
@pytest.mark.optimizers
@pytest.mark.integration
def test_optim_ode_solver(get_OC_optimizer) -> None:
"""
check if optimization result is below 1e-1
"""
opt = get_OC_optimizer
opt.fid_func = state_transfer_from_states
opt.set_goal_function(ode_solver="rk4", ode_step_function="schrodinger")
model = opt.exp.pmap.model
psi_init = [[0] * model.tot_dim]
psi_init[0][0] = 1 / np.sqrt(2)
index = model.get_state_indeces([(1, 0)])[0]
psi_init[0][index] = 1 / np.sqrt(2)
target_state = tf.transpose(tf.constant(psi_init, tf.complex128))
params = {"target": target_state}
opt.fid_func_kwargs = {"params": params}
assert opt.evaluation == 0
opt.optimize_controls()
if opt.algorithm == algorithms.single_eval:
assert opt.current_best_goal < 0.5
else:
assert opt.current_best_goal < 0.1
maxiterKey = "maxiters" if opt.algorithm == algorithms.tf_sgd else "maxiter"
assert opt.evaluation == opt.options[maxiterKey] - 1
@pytest.mark.slow
@pytest.mark.tensorflow
@pytest.mark.optimizers
@pytest.mark.integration
def test_optim_ode_solver_final(get_OC_optimizer) -> None:
"""
check if optimization result is below 1e-1
"""
opt = get_OC_optimizer
opt.fid_func = state_transfer_from_states
opt.set_goal_function(
ode_solver="rk4", ode_step_function="schrodinger", only_final_state=True
)
model = opt.exp.pmap.model
psi_init = [[0] * model.tot_dim]
psi_init[0][0] = 1 / np.sqrt(2)
index = model.get_state_indeces([(1, 0)])[0]
psi_init[0][index] = 1 / np.sqrt(2)
target_state = tf.transpose(tf.constant(psi_init, tf.complex128))
params = {"target": target_state}
opt.fid_func_kwargs = {"params": params}
assert opt.evaluation == 0
opt.optimize_controls()
if opt.algorithm == algorithms.single_eval:
assert opt.current_best_goal < 0.5
else:
assert opt.current_best_goal < 0.1
maxiterKey = "maxiters" if opt.algorithm == algorithms.tf_sgd else "maxiter"
assert opt.evaluation == opt.options[maxiterKey] - 1
@pytest.mark.tensorflow
@pytest.mark.integration
def test_rk4_unitary(get_two_qubit_chip) -> None:
"""Testing that RK4 exists and runs."""
exp = get_two_qubit_chip
pmap = exp.pmap
exp.set_prop_method(rk4_unitary)
exp.propagation(pmap.model, pmap.generator, pmap.instructions["rx90p[0]"])
@pytest.mark.tensorflow
@pytest.mark.integration
def test_ode_solver(get_two_qubit_chip) -> None:
"""Testing that ODE solver exists and runs for solvers rk4, rk5, tsit5."""
exp = get_two_qubit_chip
exp.set_opt_gates(["rx90p[0]"])
exp.compute_states(solver="rk4")
exp.compute_states(solver="rk5")
exp.compute_states(solver="tsit5")
exp.compute_states(solver="rk4", step_function="von_neumann")
exp.compute_states(solver="rk5", step_function="von_neumann")
exp.compute_states(solver="tsit5", step_function="von_neumann")
@pytest.mark.tensorflow
@pytest.mark.integration
def test_compute_final_state(get_two_qubit_chip) -> None:
"""Testing that compute_final_state exists and runs for solvers rk4, rk5, tsit5."""
exp = get_two_qubit_chip
exp.set_opt_gates(["rx90p[0]"])
exp.compute_final_state(solver="rk4")
exp.compute_final_state(solver="rk5")
exp.compute_final_state(solver="tsit5")
exp.compute_final_state(solver="rk4", step_function="von_neumann")
exp.compute_final_state(solver="rk5", step_function="von_neumann")
exp.compute_final_state(solver="tsit5", step_function="von_neumann")