def test_trainingcomplex(self):
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)
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,
)
]
self.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).item())
KLs.append(ts.KL(nn_state, target_psi, space, bases=bases).item())
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")
self.assertTrue(abs(np.average(fidelities) - 0.38) < 0.05)
self.assertTrue(abs(np.average(KLs) - 0.33) < 0.05)
self.assertTrue((np.std(fidelities) / np.sqrt(len(fidelities))) < 0.01)
self.assertTrue((np.std(KLs) / np.sqrt(len(KLs))) < 0.01)
import numpy as np
import matplotlib.pyplot as plt
from qucumber.nn_states import PositiveWaveFunction
from qucumber.callbacks import MetricEvaluator
import qucumber.utils.training_statistics as ts
import qucumber.utils.data as data
import qucumber
psi_path = "phi_fourier.txt"
train_path = "data_fourier.txt"
train_data, true_psi = data.load_data(train_path, psi_path)
nv = train_data.shape[-1]
nh = nv * 10
nn_state = PositiveWaveFunction(num_visible=nv, num_hidden=nh, gpu=False)
def psi_coefficient(nn_state, space, A, **kwargs):
norm = nn_state.compute_normalization(space).sqrt_()
return A * nn_state.psi(space)[0][0] / norm
pbs = 100
nbs = pbs
epochs = 500
lr = 0.005
k = 10
period = 50
space = nn_state.generate_hilbert_space()
def test_trainingpositive(self):
print("Positive Wavefunction")
print("---------------------")
train_samples_path = os.path.join(
__tests_location__,
"..",
"examples",
"Tutorial1_TrainPosRealWavefunction",
"tfim1d_data.txt",
)
psi_path = os.path.join(
__tests_location__,
"..",
"examples",
"Tutorial1_TrainPosRealWavefunction",
"tfim1d_psi.txt",
)
train_samples, target_psi = data.load_data(train_samples_path,
psi_path)
nv = nh = train_samples.shape[-1]
fidelities = []
KLs = []
epochs = 5
batch_size = 100
num_chains = 200
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 = PositiveWavefunction(num_visible=nv,
num_hidden=nh,
gpu=False)
space = nn_state.generate_hilbert_space(nv)
callbacks = [
MetricEvaluator(
log_every,
{
"Fidelity": ts.fidelity,
"KL": ts.KL
},
target_psi=target_psi,
space=space,
verbose=True,
)
]
self.initialize_posreal_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,
progbar=False,
callbacks=callbacks,
)
fidelities.append(ts.fidelity(nn_state, target_psi, space).item())
KLs.append(ts.KL(nn_state, target_psi, space).item())
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")
self.assertTrue(abs(np.average(fidelities) - 0.85) < 0.02)
self.assertTrue(abs(np.average(KLs) - 0.29) < 0.05)
self.assertTrue((np.std(fidelities) / np.sqrt(len(fidelities))) < 0.01)
self.assertTrue((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,
}
