def complex_wavefunction_data(gpu, num_hidden):
with open(
os.path.join(__tests_location__, "data", "test_grad_data.pkl"), "rb"
) as f:
test_data = pickle.load(f)
qucumber.set_random_seed(SEED, cpu=True, gpu=gpu, quiet=True)
data_bases = test_data["2qubits"]["train_bases"]
data_samples = torch.tensor(
test_data["2qubits"]["train_samples"], dtype=torch.double
)
bases_data = test_data["2qubits"]["bases"]
target_psi_tmp = torch.tensor(
test_data["2qubits"]["target_psi"], dtype=torch.double
)
num_visible = data_samples.shape[-1]
unitary_dict = unitaries.create_dict()
nn_state = ComplexWaveFunction(
num_visible, num_hidden, unitary_dict=unitary_dict, gpu=gpu
)
CGU = ComplexGradsUtils(nn_state)
bases = CGU.transform_bases(bases_data)
psi_dict = CGU.load_target_psi(bases, target_psi_tmp)
vis = nn_state.generate_hilbert_space(num_visible)
data_samples = data_samples.to(device=nn_state.device)
unitary_dict = {b: v.to(device=nn_state.device) for b, v in unitary_dict.items()}
psi_dict = {b: v.to(device=nn_state.device) for b, v in psi_dict.items()}
ComplexWaveFunctionFixture = namedtuple(
"ComplexWaveFunctionFixture",
[
"data_samples",
"data_bases",
"grad_utils",
"bases",
"psi_dict",
"vis",
"nn_state",
"unitary_dict",
],
)
return ComplexWaveFunctionFixture(
data_samples=data_samples,
data_bases=data_bases,
grad_utils=CGU,
bases=bases,
psi_dict=psi_dict,
vis=vis,
nn_state=nn_state,
unitary_dict=unitary_dict,
)
def complex_wavefunction_data(request, gpu, num_hidden):
with open(
os.path.join(request.fspath.dirname, "data", "test_grad_data.pkl"),
"rb") as f:
test_data = pickle.load(f)
qucumber.set_random_seed(SEED, cpu=True, gpu=gpu, quiet=True)
data_bases = test_data["2qubits"]["train_bases"]
data_samples = torch.tensor(test_data["2qubits"]["train_samples"],
dtype=torch.double)
all_bases = test_data["2qubits"]["bases"]
target_psi_tmp = torch.tensor(test_data["2qubits"]["target_psi"],
dtype=torch.double).t()
num_visible = data_samples.shape[-1]
nn_state = ComplexWaveFunction(num_visible, num_hidden, gpu=gpu)
unitary_dict = nn_state.unitary_dict
CGU = ComplexGradsUtils(nn_state)
all_bases = CGU.transform_bases(all_bases)
target = CGU.load_target_psi(all_bases, target_psi_tmp)
target = {b: v.to(device=nn_state.device) for b, v in target.items()}
space = nn_state.generate_hilbert_space()
data_samples = data_samples.to(device=nn_state.device)
ComplexWaveFunctionFixture = namedtuple(
"ComplexWaveFunctionFixture",
[
"data_samples",
"data_bases",
"grad_utils",
"all_bases",
"target",
"space",
"nn_state",
"unitary_dict",
],
)
return ComplexWaveFunctionFixture(
data_samples=data_samples,
data_bases=data_bases,
grad_utils=CGU,
all_bases=all_bases,
target=target,
space=space,
nn_state=nn_state,
unitary_dict=unitary_dict,
)
def test_complex_training_without_bases_fail():
qucumber.set_random_seed(SEED, cpu=True, gpu=False, quiet=True)
np.random.seed(SEED)
nn_state = ComplexWaveFunction(10, gpu=False)
data = torch.ones(100, 10)
msg = "Training ComplexWaveFunction without providing bases should fail!"
with pytest.raises(ValueError, message=msg):
nn_state.fit(data, epochs=1, pos_batch_size=10, input_bases=None)
def test_complex_wavefunction(gpu):
qucumber.set_random_seed(SEED, cpu=True, gpu=gpu, quiet=True)
np.random.seed(SEED)
nn_state = ComplexWaveFunction(10, gpu=gpu)
old_params = parameters_to_vector(nn_state.rbm_am.parameters())
data = torch.ones(100, 10)
# generate sample bases randomly, with probability 0.9 of being 'Z', otherwise 'X'
bases = np.where(np.random.binomial(1, 0.9, size=(100, 10)), "Z", "X")
nn_state.fit(data, epochs=1, pos_batch_size=10, input_bases=bases)
new_params = parameters_to_vector(nn_state.rbm_am.parameters())
msg = "ComplexWaveFunction's parameters did not change!"
assert not torch.equal(old_params, new_params), msg
def test_complex_warn_on_gpu():
with pytest.warns(ResourceWarning):
ComplexWaveFunction(10, gpu=True)
def test_trainingcomplex(vectorized):
print("Complex WaveFunction")
print("--------------------")
train_samples_path = os.path.join(
__tests_location__,
"..",
"examples",
"Tutorial2_TrainComplexWaveFunction",
"qubits_train.txt",
)
train_bases_path = os.path.join(
__tests_location__,
"..",
"examples",
"Tutorial2_TrainComplexWaveFunction",
"qubits_train_bases.txt",
)
bases_path = os.path.join(
__tests_location__,
"..",
"examples",
"Tutorial2_TrainComplexWaveFunction",
"qubits_bases.txt",
)
psi_path = os.path.join(
__tests_location__,
"..",
"examples",
"Tutorial2_TrainComplexWaveFunction",
"qubits_psi.txt",
)
train_samples, target_psi, train_bases, bases = data.load_data(
train_samples_path, psi_path, train_bases_path, bases_path)
unitary_dict = unitaries.create_dict()
nv = nh = train_samples.shape[-1]
fidelities = []
KLs = []
epochs = 5
batch_size = 50
num_chains = 10
CD = 10
lr = 0.1
log_every = 5
print("Training 10 times and checking fidelity and KL at 5 epochs...\n")
for i in range(10):
print("Iteration: ", i + 1)
nn_state = ComplexWaveFunction(unitary_dict=unitary_dict,
num_visible=nv,
num_hidden=nh,
gpu=False)
if not vectorized:
nn_state.debug_gradient_rotation = True
space = nn_state.generate_hilbert_space(nv)
callbacks = [
MetricEvaluator(
log_every,
{
"Fidelity": ts.fidelity,
"KL": ts.KL
},
target_psi=target_psi,
bases=bases,
space=space,
verbose=True,
)
]
initialize_complex_params(nn_state)
nn_state.fit(
data=train_samples,
epochs=epochs,
pos_batch_size=batch_size,
neg_batch_size=num_chains,
k=CD,
lr=lr,
time=True,
input_bases=train_bases,
progbar=False,
callbacks=callbacks,
)
fidelities.append(ts.fidelity(nn_state, target_psi, space))
KLs.append(ts.KL(nn_state, target_psi, space, bases=bases))
print("\nStatistics")
print("----------")
print(
"Fidelity: ",
np.average(fidelities),
"+/-",
np.std(fidelities) / np.sqrt(len(fidelities)),
"\n",
)
print("KL: ", np.average(KLs), "+/-",
np.std(KLs) / np.sqrt(len(KLs)), "\n")
assert abs(np.average(fidelities) - 0.38) < 0.05
assert abs(np.average(KLs) - 0.33) < 0.05
assert (np.std(fidelities) / np.sqrt(len(fidelities))) < 0.01
assert (np.std(KLs) / np.sqrt(len(KLs))) < 0.01
def quantum_state_training_data(request):
nn_state_type = request.param
if nn_state_type == PositiveWaveFunction:
root = os.path.join(
request.fspath.dirname,
"..",
"examples",
"Tutorial1_TrainPosRealWaveFunction",
)
train_samples, target = data.load_data(
tr_samples_path=os.path.join(root, "tfim1d_data.txt"),
tr_psi_path=os.path.join(root, "tfim1d_psi.txt"),
)
train_bases, bases = None, None
nn_state = PositiveWaveFunction(num_visible=train_samples.shape[-1],
gpu=False)
batch_size, num_chains = 100, 200
fid_target, kl_target = 0.85, 0.29
reinit_params_fn = initialize_posreal_params
elif nn_state_type == ComplexWaveFunction:
root = os.path.join(
request.fspath.dirname,
"..",
"examples",
"Tutorial2_TrainComplexWaveFunction",
)
train_samples, target, train_bases, bases = data.load_data(
tr_samples_path=os.path.join(root, "qubits_train.txt"),
tr_psi_path=os.path.join(root, "qubits_psi.txt"),
tr_bases_path=os.path.join(root, "qubits_train_bases.txt"),
bases_path=os.path.join(root, "qubits_bases.txt"),
)
nn_state = ComplexWaveFunction(num_visible=train_samples.shape[-1],
gpu=False)
batch_size, num_chains = 50, 10
fid_target, kl_target = 0.38, 0.33
reinit_params_fn = initialize_complex_params
elif nn_state_type == DensityMatrix:
root = os.path.join(request.fspath.dirname, "..", "examples",
"Tutorial3_TrainDensityMatrix")
train_samples, target, train_bases, bases = data.load_data_DM(
tr_samples_path=os.path.join(root,
"N2_W_state_100_samples_data.txt"),
tr_mtx_real_path=os.path.join(root, "N2_W_state_target_real.txt"),
tr_mtx_imag_path=os.path.join(root, "N2_W_state_target_imag.txt"),
tr_bases_path=os.path.join(root,
"N2_W_state_100_samples_bases.txt"),
bases_path=os.path.join(root, "N2_IC_bases.txt"),
)
nn_state = DensityMatrix(num_visible=train_samples.shape[-1],
gpu=False)
batch_size, num_chains = 100, 10
fid_target, kl_target = 0.45, 0.42
def reinit_params_fn(request, nn_state):
nn_state.reinitialize_parameters()
else:
raise ValueError(
f"invalid test config: {nn_state_type} is not a valid quantum state type"
)
return {
"nn_state": nn_state,
"data": train_samples,
"input_bases": train_bases,
"target": target,
"bases": bases,
"epochs": 5,
"pos_batch_size": batch_size,
"neg_batch_size": num_chains,
"k": 10,
"lr": 0.1,
"space": nn_state.generate_hilbert_space(),
"fid_target": fid_target,
"kl_target": kl_target,
"reinit_params_fn": reinit_params_fn,
}