def test_stochastic_descent_neg_max_num_flips(self):
# Every 2-SAT with just one clause will not be satisfied by 1/4 of all
# possible literal assignments.
# With only one bang, the only way to do better than random guessing is to
# apply the DNF hamiltonian.
dnf = dnf_lib.DNF(3, [dnf_lib.Clause(0, 1, False, True)])
circuit = dnf_circuit_lib.BangBangProtocolCircuit(1, dnf)
with self.assertRaisesRegex(
ValueError, 'max_num_flips should be positive, not -10'):
stochastic_descent_lib.stochastic_descent(
circuit=circuit,
max_num_flips=-10,
initial_protocol=stochastic_descent_lib.get_random_protocol(5),
minimize=False)
def main(argv):
if len(argv) > 1:
raise app.UsageError('Too many command-line arguments.')
logging.info('Generate a random DNF')
dnf = dnf_lib.get_random_dnf(FLAGS.num_literals, FLAGS.num_clauses)
logging.info(dnf)
logging.info('initialization_method: %s', FLAGS.initialization_method)
if FLAGS.initialization_method == 'random':
get_initial_protocol = stochastic_descent_lib.get_random_protocol
elif FLAGS.initialization_method == 'adiabatic':
get_initial_protocol = functools.partial(
stochastic_descent_lib.get_random_adiabatic_protocol, ascending=True)
elif FLAGS.initialization_method == 'anti_adiabatic':
get_initial_protocol = functools.partial(
stochastic_descent_lib.get_random_adiabatic_protocol, ascending=False)
logging.info('Stochastic descent')
for i in range(FLAGS.num_samples):
logging.info('Trial index: %d', i)
bangbang_protocol, protocol_eval, num_epoch = (
stochastic_descent_lib.stochastic_descent(
circuit=dnf_circuit_lib.BangBangProtocolCircuit(
chunk_time=FLAGS.total_time / FLAGS.num_chunks, dnf=dnf),
max_num_flips=FLAGS.max_num_flips,
initial_protocol=get_initial_protocol(FLAGS.num_chunks),
minimize=False,
skip_search=FLAGS.skip_search))
logging.info(
'Optimal protocol: %s',
circuit_lib.protocol_to_string(bangbang_protocol))
logging.info('Protocol eval: %f', protocol_eval)
logging.info('Number of epoch: %d', num_epoch)
def test_stochastic_descent_skip_search(self):
dnf = dnf_lib.DNF(2, [dnf_lib.Clause(0, 1, True, True)])
circuit = dnf_circuit_lib.BangBangProtocolCircuit(1, dnf)
random_protocol = stochastic_descent_lib.get_random_protocol(2)
random_eval = circuit.get_constraint_expectation(
circuit.get_wavefunction(random_protocol))
protocol, evaluation, num_epoch = stochastic_descent_lib.stochastic_descent(
circuit=circuit,
max_num_flips=1,
initial_protocol=random_protocol,
minimize=False,
skip_search=True)
self.assertListEqual(protocol, random_protocol)
self.assertIsInstance(evaluation, float)
self.assertAlmostEqual(evaluation, random_eval)
# Zero epoch of stochastic descent.
self.assertEqual(num_epoch, 0)
def test_stochastic_descent_maximize(self):
# Every 2-SAT with just one clause will not be satisfied by 1/4 of all
# possible literal assignments.
# With only one bang, the only way to do better than random guessing is to
# apply the DNF hamiltonian.
dnf = dnf_lib.DNF(2, [dnf_lib.Clause(0, 1, True, True)])
circuit = dnf_circuit_lib.BangBangProtocolCircuit(1, dnf)
random_protocol = stochastic_descent_lib.get_random_protocol(2)
random_eval = circuit.get_constraint_expectation(
circuit.get_wavefunction(random_protocol))
protocol, evaluation, num_epoch = stochastic_descent_lib.stochastic_descent(
circuit=circuit,
max_num_flips=2,
initial_protocol=stochastic_descent_lib.get_random_protocol(5),
minimize=False)
self.assertLen(protocol, 5)
self.assertIsInstance(evaluation, float)
self.assertGreaterEqual(evaluation, random_eval)
# Contain at least 1 epoch of stochastic descent.
self.assertGreaterEqual(num_epoch, 1)