def test_batching_wave_functions_and_parameters():
N = 3
d = 13
qc = QuantumCircuit(N, [RX(wires=[
0,
]), RX(wires=[
1,
])],
batch_params=True,
device="CPU")
psi = tf.placeholder(dtype=tf.complex64,
shape=(None, *[2 for _ in range(N)]))
theta = tf.convert_to_tensor([np.pi, np.pi])
with pytest.raises(
AssertionError,
match=
"expected tensor with 3 dimensions, received tensor with shape"):
qc.set_parameters(theta)
qc.set_parameters(tf.reshape(theta, (1, 2, 1)))
with pytest.raises(
AssertionError,
match=
"We cannot have a batch of wave functions and a batch of parameters at the same time"
):
qc.circuit(psi)
def test_batch_params_argument():
N = 3
theta = tf.placeholder(dtype=tf.float32, shape=(None, 2, 1))
qc = QuantumCircuit(N, [RX(wires=[
0,
]), RX(wires=[
1,
])])
with pytest.raises(
AssertionError,
match=
" pass the batch_params=True argument to the QuantumCircuit class to enable batches of parameters."
):
qc.set_parameters(theta)
qc.circuit(qc.phi)
def test_external_input_dtype():
N = 3
qc = QuantumCircuit(N, [RX(wires=[
0,
]), RX(wires=[
1,
])],
batch_params=True,
device="CPU")
with pytest.raises(TypeError,
match="External input Tensor must have type "):
theta = tf.placeholder(dtype=tf.complex64, shape=(None, 2, 1))
qc.set_parameters(theta)
with pytest.raises(TypeError,
match="External input Tensor must have type "):
theta = tf.placeholder(dtype=tf.float64, shape=(None, 2, 1))
qc.set_parameters(theta)
def test_hybrid_neural_network_gradient_tracking():
N = 2
gates = [RX(wires=[
0,
]), RX(wires=[
1,
])]
qc = QuantumCircuit(N, gates=gates, tensorboard=True, device="CPU")
# Make a neural network
NN = tf.keras.Sequential([
tf.keras.layers.Dense(qc.nparams * 10,
activation=tf.keras.activations.tanh),
tf.keras.layers.Dense(2, activation=tf.keras.activations.tanh),
tf.keras.layers.Lambda(lambda x: x * 2 * np.pi)
])
x_in = tf.ones((qc.nparams, 1)) * 0.01
theta = NN(x_in)
theta = tf.reduce_mean(theta, axis=0)
theta = tf.reshape(theta, (1, -1, 1))
qc.set_parameters(theta)
phi = qc.circuit(qc.phi)
obs = Observable("x", wires=[
0,
]), Observable("x", wires=[
1,
]), Observable("z", wires=[
1,
])
expval_layer = ExpectationValue(obs)
expvals = expval_layer(phi)
# Get energy for each timstep
energy = tf.reduce_sum(expvals)
opt = tf.compat.v1.train.AdamOptimizer(0.0001)
grads = opt.compute_gradients(energy)
qc.initialize()
g = qc._sess.run(grads)
def test_multiple_thetas():
N = 3
d = 13
theta = tf.placeholder(dtype=tf.float32, shape=(None, 2, 1))
qc = QuantumCircuit(N, [RX(wires=[
0,
]), RX(wires=[
1,
])],
batch_params=True,
device="CPU")
qc.set_parameters(theta)
phi_dim_1 = qc.circuit(qc.phi)
obs = [
Observable("x", wires=[
0,
]),
Observable("x", wires=[
1,
]),
Observable("x", wires=[
2,
]),
Observable("y", wires=[
0,
]),
Observable("y", wires=[
1,
]),
Observable("y", wires=[
2,
])
]
expval_layer = ExpectationValue(obs)
expvals_dim_1 = expval_layer(phi_dim_1)
qc.initialize()
qc._sess.run([expvals_dim_1], feed_dict={theta: np.random.randn(d, 2, 1)})