def qubo():
model = LogicalModel(mtype="qubo")
x = model.variables(name="x", shape=(3, ))
model.delete_variable(x[0])
model.add_interaction(x[1], coefficient=1.0)
model.add_interaction(x[2], coefficient=2.0)
model.add_interaction((x[1], x[2]), coefficient=3.0)
return model.to_physical()
def _create_ising_model_for_eq():
model = LogicalModel(mtype="ising")
x = model.variables(name="x", shape=(3, ))
model.delete_variable(x[0])
model.add_interaction(x[1], coefficient=1.0)
model.add_interaction(x[2], coefficient=2.0)
model.add_interaction((x[1], x[2]), coefficient=3.0)
return model.to_physical()
def test_logical_model_variables_from_pyqubo(vartype, mtype):
x = pyqubo.Array.create("x", shape=(2, 3), vartype=vartype)
model = LogicalModel(mtype=mtype)
x_when_applied = model.variables(x)
x_from_model = model.get_variables_by_name("x")
assert id(x) == id(x_when_applied)
assert x == x_when_applied
assert id(x) == id(x_from_model)
assert x == x_from_model
def test_logical_model_invalid_mtype():
with pytest.raises(ValueError):
LogicalModel()
with pytest.raises(ValueError):
LogicalModel(mtype="invalidtype")
with pytest.raises(ValueError):
LogicalModel(mtype=12345)
def ising_x44():
model = LogicalModel(mtype="ising")
x = model.variables("x", shape=(4, 4))
model.add_interaction(x[0, 0], coefficient=20.0)
model.add_interaction((x[1, 1], x[2, 2]), coefficient=21.0, attributes={"foo2": "bar2"})
model.add_interaction(x[3, 3], coefficient=22.0, scale=2.0, timestamp=12345, attributes={"myattr": "mymymymy"})
return model
def test_logical_model_variables_append(initial_shape, additional_shape, expected_shape):
model = LogicalModel(mtype="ising")
model.variables("x", shape=initial_shape)
assert "x" in model.get_variables()
assert model.get_variables_by_name("x").shape == initial_shape
model.append("x", shape=additional_shape)
assert model.get_variables_by_name("x").shape == expected_shape
def physical():
model = LogicalModel(mtype="qubo")
x = model.variables("x", shape=(2, ))
model.add_interaction(x[0], coefficient=1.0)
model.add_interaction((x[0], x[1]), coefficient=-1.0)
physical = model.to_physical()
return physical
def test_local_solver_default_beta_range():
model = LogicalModel(mtype="ising")
s = model.variables("s", shape=(2,))
model.add_interaction(s[0], coefficient=1.0)
model.add_interaction(s[1], coefficient=2.0)
model.add_interaction((s[0], s[1]), coefficient=-3.0)
solver = LocalSolver()
beta_range = solver.default_beta_range(model.to_physical())
assert beta_range == [0.13862943611198905, 4.605170185988092]
def test_sawatabi_solver_ising():
model = LogicalModel(mtype="ising")
s = model.variables("s", shape=(2,))
model.add_interaction(s[0], coefficient=1.0)
model.add_interaction(s[1], coefficient=2.0)
model.add_interaction((s[0], s[1]), coefficient=-3.0)
model.offset(10.0)
solver = SawatabiSolver()
sampleset = solver.solve(model.to_physical(), num_reads=2, num_sweeps=10, cooling_rate=0.5, initial_temperature=10.0, seed=12345)
assert sampleset.variables == ["s[0]", "s[1]"]
assert len(sampleset.record) == 1
# Check the ground state
assert np.array_equal(sampleset.record[0].sample, [-1, 1])
assert sampleset.record[0].energy == 6.0
assert sampleset.record[0].num_occurrences == 2
def test_sawatabi_solver_qubo():
model = LogicalModel(mtype="qubo")
x = model.variables("x", shape=(2,))
model.add_interaction(x[0], coefficient=1.0)
model.add_interaction(x[1], coefficient=2.0)
model.add_interaction((x[0], x[1]), coefficient=-5.0)
model.offset(10.0)
solver = SawatabiSolver()
sampleset = solver.solve(model.to_physical(), num_reads=1, num_sweeps=10, cooling_rate=0.55, seed=12345)
assert sampleset.variables == ["x[0]", "x[1]"]
assert len(sampleset.record) == 1
# Check the ground state
record = sorted(sampleset.record, key=lambda r: r.energy) # sort by energy
assert np.array_equal(record[0].sample, [0, 1])
assert record[0].energy == 8.0
def test_local_solver_sa_qubo():
model = LogicalModel(mtype="qubo")
x = model.variables("x", shape=(2,))
model.add_interaction(x[0], coefficient=1.0)
model.add_interaction(x[1], coefficient=2.0)
model.add_interaction((x[0], x[1]), coefficient=-5.0)
model.offset(10.0)
solver = LocalSolver(exact=False)
sampleset = solver.solve(model.to_physical(), seed=12345)
assert sampleset.variables == ["x[0]", "x[1]"]
assert len(sampleset.record) == 1
# Check the ground state
assert np.array_equal(sampleset.record[0].sample, [0, 1])
assert sampleset.record[0].energy == 8.0
assert sampleset.record[0].num_occurrences == 1
def test_logical_model_variables(shape):
model = LogicalModel(mtype="ising")
x = model.variables("x", shape=shape)
assert len(model.get_variables()) == 1
assert "x" in model.get_variables()
assert x.shape == shape
assert isinstance(x, pyqubo.Array)
assert model.get_variables_by_name("x") == x
assert id(model.get_variables_by_name("x")) == id(x)
assert id(model.get_variables()["x"]) == id(x)
def _create_nxn_random_lattice_model(n=4, seed=None):
if seed is not None:
random.seed(seed)
model = LogicalModel(mtype="qubo")
x = model.variables("x", shape=(n, n))
for i in range(n):
for j in range(n):
model.add_interaction(x[i, j], coefficient=random.random())
if 0 < i:
model.add_interaction((x[i - 1, j], x[i, j]),
coefficient=random.random())
if 0 < j:
model.add_interaction((x[i, j - 1], x[i, j]),
coefficient=random.random())
def test_sawatabi_solver_n_hot_ising_with_deleting(n, s, i):
# n out of (s - 1) variables should be 1
model = LogicalModel(mtype="ising")
x = model.variables("x", shape=(s,))
model.add_constraint(NHotConstraint(variables=x, n=n))
model.delete_variable(x[i])
solver = SawatabiSolver()
sampleset = solver.solve(model.to_physical(), seed=12345)
result = np.array(sampleset.record[0].sample)
assert np.count_nonzero(result == 1) == n
assert np.count_nonzero(result == -1) == s - n - 1
def test_sawatabi_solver_with_initial_states_qubo():
model = LogicalModel(mtype="qubo")
x = model.variables("x", shape=(6,))
for i in range(6):
model.add_interaction(x[i], coefficient=-1.0)
solver = SawatabiSolver()
initial_states = [
{
"x[0]": 1,
"x[1]": 0,
"x[2]": 1,
"x[3]": 0,
"x[4]": 1,
"x[5]": 0,
},
]
sampleset = solver.solve(model.to_physical(), num_reads=1, num_sweeps=10, cooling_rate=0.5, initial_states=initial_states, seed=12345)
assert len(sampleset.record) == 1
# Check the ground state
assert np.array_equal(sampleset.record[0].sample, [0, 0, 0, 0, 0, 0])
assert sampleset.record[0].energy == 0.0
assert sampleset.record[0].num_occurrences == 1
def test_sawatabi_solver_with_initial_states_reverse():
model = LogicalModel(mtype="ising")
x = model.variables("x", shape=(6,))
for i in range(6):
model.add_interaction(x[i], coefficient=10.0)
solver = SawatabiSolver()
initial_states = [
{
"x[0]": -1,
"x[1]": -1,
"x[2]": -1,
"x[3]": -1,
"x[4]": -1,
"x[5]": -1,
},
]
sampleset = solver.solve(
model.to_physical(),
num_reads=1,
num_sweeps=10,
initial_states=initial_states,
reverse_options={"reverse_period": 5, "reverse_temperature": 10.0},
seed=12345,
)
assert len(sampleset.record) == 1
# Check the ground state
assert np.array_equal(sampleset.record[0].sample, [1, 1, 1, 1, 1, 1])
assert sampleset.record[0].energy == -60.0
assert sampleset.record[0].num_occurrences == 1
def test_logical_model_variables_append_without_initialize(shape):
model = LogicalModel(mtype="ising")
# The following operation will be successful with a UserWarning.
with pytest.warns(UserWarning):
model.append("x", shape=shape)
assert "x" in model.get_variables()
assert model.get_variables_by_name("x").shape == shape
def test_sawatabi_solver_with_initial_states_ising():
model = LogicalModel(mtype="ising")
x = model.variables("x", shape=(12,))
for i in range(12):
model.add_interaction(x[i], coefficient=-1.0)
solver = SawatabiSolver()
initial_states = [
{
"x[0]": 1,
"x[1]": -1,
"x[2]": -1,
"x[3]": -1,
"x[4]": -1,
"x[5]": -1,
"x[6]": -1,
"x[7]": -1,
"x[8]": -1,
"x[9]": -1,
"x[10]": -1,
"x[11]": -1,
},
]
sampleset = solver.solve(model.to_physical(), num_reads=1, num_sweeps=1, pickup_mode="sequential", initial_temperature=1e-9, initial_states=initial_states)
assert len(sampleset.record) == 1
# Check the ground state
assert np.array_equal(sampleset.record[0].sample, [-1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1])
assert sampleset.record[0].energy == -12.0
assert sampleset.record[0].num_occurrences == 1
def _create_n_variable_random_complete_model(n=4, seed=None):
if seed is not None:
random.seed(seed)
model = LogicalModel(mtype="ising")
x = model.variables("x", shape=(n, ))
for i in range(n):
model.add_interaction(x[i], coefficient=random.random())
for i in range(n - 1):
for j in range(i + 1, n):
model.add_interaction((x[i], x[j]), coefficient=random.random())
def test_optigan_solver_with_empty_config_fails():
model = LogicalModel(mtype="qubo")
x = model.variables("x", shape=(2, ))
model.add_interaction((x[0], x[1]), coefficient=-1.0)
physical = model.to_physical()
solver = OptiganSolver(config="/tmp/.optigan.yml")
with pytest.raises(FileNotFoundError):
solver.solve(physical)
def test_optigan_solver_with_ising_model_fails():
model = LogicalModel(mtype="ising")
s = model.variables("s", shape=(2, ))
model.add_interaction((s[0], s[1]), coefficient=-1.0)
physical = model.to_physical()
solver = OptiganSolver()
with pytest.raises(ValueError):
solver.solve(physical)
def test_sawatabi_solver_invalid_reverse_options():
model = LogicalModel(mtype="ising")
x = model.variables("x", shape=(2,))
for i in range(2):
model.add_interaction(x[i], coefficient=-1.0)
solver = SawatabiSolver()
with pytest.raises(ValueError):
solver.solve(model.to_physical(), reverse_options={"reverse_period": 5})
with pytest.raises(ValueError):
solver.solve(model.to_physical(), reverse_options={"reverse_temperature": 10.0})
def test_sawatabi_solver_invalid_pickup_mode():
model = LogicalModel(mtype="ising")
x = model.variables("x", shape=(2,))
for i in range(2):
model.add_interaction(x[i], coefficient=-1.0)
solver = SawatabiSolver()
with pytest.raises(ValueError):
solver.solve(model.to_physical(), pickup_mode="invalid")
def test_sawatabi_solver_with_initial_states_fails():
model = LogicalModel(mtype="ising")
x = model.variables("x", shape=(2,))
for i in range(2):
model.add_interaction(x[i], coefficient=-1.0)
solver = SawatabiSolver()
initial_states = [{"x[0]": 1, "x[1]": 1}]
with pytest.raises(ValueError):
solver.solve(model.to_physical(), num_reads=2, initial_states=initial_states)
def test_sawatabi_solver_reuse_solver_instance():
model = LogicalModel(mtype="ising")
x = model.variables("x", shape=(2,))
for i in range(2):
model.add_interaction(x[i], coefficient=-1.0)
solver = SawatabiSolver()
sampleset_1 = solver.solve(model.to_physical(), num_reads=1, num_sweeps=10, cooling_rate=0.5, seed=12345)
assert np.array_equal(sampleset_1.record[0].sample, [-1, -1])
assert sampleset_1.record[0].energy == -2.0
sampleset_2 = solver.solve(model.to_physical(), num_reads=1, num_sweeps=10, cooling_rate=0.5, seed=12345)
assert np.array_equal(sampleset_2.record[0].sample, [-1, -1])
assert sampleset_2.record[0].energy == -2.0
def test_local_solver_n_hot_ising_with_deleting(n, s, i):
# n out of (s - 1) variables should be 1
model = LogicalModel(mtype="ising")
x = model.variables("x", shape=(s,))
model.add_constraint(NHotConstraint(variables=x, n=n))
model.delete_variable(x[i])
solver = LocalSolver()
physical = model.to_physical()
for seed in [11, 22, 33, 44, 55]:
sampleset = solver.solve(physical, seed=seed)
result = np.array(sampleset.record[0].sample)
assert np.count_nonzero(result == 1) == n
assert np.count_nonzero(result == -1) == s - n - 1
# Execution time should be within practical seconds (20 sec).
assert sampleset.info["timing"]["execution_sec"] <= 10.0
def test_sawatabi_solver_with_stats():
model = LogicalModel(mtype="ising")
x = model.variables("x", shape=(2,))
for i in range(2):
model.add_interaction(x[i], coefficient=-1.0)
solver = SawatabiSolver()
sampleset, stats = solver.solve(model.to_physical(), num_reads=1, num_sweeps=10, cooling_rate=0.5, seed=12345, need_stats=True)
assert stats[0]["acceptance_history"][-1] == 0
assert stats[0]["energy_history"][-1] == -2.0
assert stats[0]["temperature_history"] == [100.0, 50.0, 25.0, 12.5, 6.25, 3.125, 1.5625, 0.78125, 0.390625, 0.1953125]
def test_logical_model_empty(mtype):
model = LogicalModel(mtype=mtype)
x = model.variables("x", shape=(10, 10))
model.add_interaction(x[0, 0], coefficient=10.0)
empty_model = model.empty()
assert empty_model.get_mtype() == mtype
assert len(empty_model._variables) == 0
assert len(empty_model._interactions_array) == len(empty_model._default_keys)
for k, v in empty_model._interactions_array.items():
assert len(v) == 0
assert empty_model._interactions_length == 0
def test_sawatabi_solver_qubo_without_active_var():
model = LogicalModel(mtype="qubo")
x = model.variables("x", shape=(2, 2))
model.add_interaction(x[0, 1], coefficient=1.0)
model.add_interaction((x[1, 0], x[1, 1]), coefficient=2.0)
physical = model.to_physical()
assert len(physical._label_to_index) == 3
assert len(physical._index_to_label) == 3
solver = SawatabiSolver()
sampleset = solver.solve(physical, seed=12345)
assert sampleset.variables == ["x[0][1]", "x[1][0]", "x[1][1]"]
assert len(sampleset.record) == 1
# Check the ground state
assert np.array_equal(sampleset.record[0].sample, [1, 1, 1])
assert sampleset.record[0].energy == -3.0
assert sampleset.record[0].num_occurrences == 1
def test_local_solver_exact_qubo():
model = LogicalModel(mtype="qubo")
x = model.variables("x", shape=(2,))
model.add_interaction(x[0], coefficient=1.0)
model.add_interaction(x[1], coefficient=2.0)
model.add_interaction((x[0], x[1]), coefficient=-5.0)
solver = LocalSolver(exact=True)
sampleset = solver.solve(model.to_physical())
assert sampleset.variables == ["x[0]", "x[1]"]
assert len(sampleset.record) == 4
for r in sampleset.record:
# Check the ground state
if np.array_equal(r.sample, [0, 1]):
assert r.energy == -2.0
assert r.num_occurrences == 1
break
else:
assert False
