def circuit17(params, wires):
"""Circuit template #17 in 1905.10876
Args:
params (array): An array of shapes (2N + floor(N/2) + floor((N-1)/2), ).
wires (Iterable): Wires that the template acts on.
"""
# Input Checks
wires, size = Wires(wires), len(wires)
check_shape(
params,
target_shape=(2 * size + floor(size / 2) + floor((size - 1) / 2), ),
msg=
f"params must be of shape {(2*size + floor(size/2) + floor((size-1)/2),)}."
)
# Define the circuit
AngleEmbedding(params[:size], wires, rotation='X')
AngleEmbedding(params[size:2 * size], wires, rotation='Z')
pattern = [[i + 1, i] for i in range(0, len(wires) - 1, 2)]
qml.broadcast(unitary=qml.CRX,
pattern=pattern,
wires=wires,
parameters=params[2 * size:2 * size + floor(size / 2)])
pattern = [[i + 1, i] for i in range(1, len(wires) - 1, 2)]
qml.broadcast(unitary=qml.CRX,
pattern=pattern,
wires=wires,
parameters=params[2 * size + floor(size / 2):])
def circuit12(params, wires):
"""Circuit template #12 in 1905.10876
Args:
params (array): An array of shapes (4N-4,).
wires (Iterable): Wires that the template acts on.
"""
# Input Checks
wires, size = Wires(wires), len(wires)
check_shape(params,
target_shape=(4 * size - 4, ),
msg=f"params must be of shape {(4 * size - 4, )}.")
# Define the circuit
AngleEmbedding(params[:size], wires, rotation='Y')
AngleEmbedding(params[size:2 * size], wires, rotation='Z')
pattern = [[i + 1, i] for i in range(0, len(wires) - 1, 2)]
qml.broadcast(unitary=qml.CZ, pattern=pattern, wires=wires)
AngleEmbedding(params[2 * size:3 * size - 2],
wires=wires[1:-1],
rotation='Y')
AngleEmbedding(params[3 * size - 2:4 * size - 4],
wires=wires[1:-1],
rotation='Z')
pattern = [[i + 1, i] for i in range(1, len(wires) - 1, 2)]
qml.broadcast(unitary=qml.CZ, pattern=pattern, wires=wires)
def circuit(params):
"""Parametrized circuit."""
for layer in range(n):
qml.broadcast(qml.RX, pattern="single", wires=self.all_wires, parameters=params[layer])
for _layer in range(n):
qml.broadcast(qml.CNOT, pattern="double", wires=self.all_wires)
return [bu.expval(qml.PauliZ(w)) for w in self.all_wires]
def circuit15(params, wires):
"""Circuit template #15 in 1905.10876
Args:
params (array): An array of shapes (2N, ).
wires (Iterable): Wires that the template acts on.
"""
# Input Checks
wires, size = Wires(wires), len(wires)
check_shape(params,
target_shape=(2 * size, ),
msg=f"params must be of shape {(2*size,)}.")
# Define the circuit
AngleEmbedding(params[:size], wires, rotation='Y')
pattern = [[i, (i + 1) % len(wires)] for i in reversed(range(len(wires)))]
qml.broadcast(unitary=qml.CNOT, pattern=pattern, wires=wires)
AngleEmbedding(params[size:], wires, rotation='Y')
pattern = [[(i - 1) % len(wires), (i - 2) % len(wires)]
for i in range(len(wires))]
qml.broadcast(unitary=qml.CNOT, pattern=pattern, wires=wires)
def mera(params, wires):
# Reformat the parameters list into a list of lists.
# Each two-qubit gate should receive a list of parameters.
# Apply the two qubit gates into the pattern of a tree.
num_gates = len(pattern)
if fix_layers:
gate_size = len(params) // (2 * depth - 1
) #determine 2qubit gate size
parameters = []
parameters.append(params[:gate_size])
for layer in range(1, depth):
gates_per_layer = 2**layer
#isometry
for i in range(gates_per_layer):
parameters.append(
params[gate_size * (2 * layer - 1):gate_size * 2 *
layer])
#unitary
for i in range(gates_per_layer - int(not periodic)):
parameters.append(params[gate_size * 2 * layer:gate_size *
(2 * layer + 1)])
else:
parameters = params
gate_size = len(params) // num_gates # determine 2qubit gate size
parameters = np.reshape(parameters, [num_gates, gate_size])
qml.broadcast(unitary=two_qubit_gate, pattern=pattern, wires=wires, parameters=parameters,\
kwargs=None)
def ttn(params, wires):
# Reformat the parameters list into a list of lists.
# Each two-qubit gate should receive a list of parameters.
parameters = []
if fix_layers:
num_params_per_layer = len(params) // depth
for layer in range(depth):
num_gates_in_layer = 2**layer
params_gate = [
params[j]
for j in range(layer * num_params_per_layer, (layer + 1) *
num_params_per_layer)
]
parameters.extend([params_gate] * num_gates_in_layer)
else:
num_params_per_gate = len(params) // len(pattern)
parameters = [[
params[i * num_params_per_gate + j]
for j in range(num_params_per_gate)
] for i in range(len(pattern))]
# Apply the two qubit gates into the pattern of a tree.
qml.broadcast(two_qubit_gate,
wires,
pattern,
parameters=parameters,
kwargs=None)
def circuit06(params, wires):
"""Circuit template #06 in 1905.10876
Args:
params (array): An array of shapes (N^2 + 3N, 1).
wires (Iterable): Wires that the template acts on.
"""
# Input Checks
wires, size = Wires(wires), len(wires)
check_shape(params,
target_shape=(size * (size + 3), ),
msg=f"params must be of shape {(size * (size + 3), )}.")
# Define the circuit
AngleEmbedding(params[:size], wires, rotation='X')
AngleEmbedding(params[size:2 * size], wires, rotation='Z')
idx_start = 2 * size
for cnt, controlled in enumerate(reversed(range(len(wires)))):
pattern = [[controlled, j] for j in reversed(range(len(wires)))
if j != controlled]
qml.broadcast(unitary=qml.CRX,
pattern=pattern,
wires=wires,
parameters=params[idx_start:idx_start + (size - 1)])
idx_start += size - 1
AngleEmbedding(params[idx_start:idx_start + size], wires, rotation='X')
AngleEmbedding(params[idx_start + size:idx_start + 2 * size],
wires,
rotation='Z')
def layer(self, features, weights):
for k in range(self.n_qubits):
upload = weights[k * 6:k * 6 +
3] + weights[k * 6 + 3:(k + 1) *
6] * features[k * 3:(k + 1) * 3]
qml.Rot(*upload, wires=self.wires[k])
qml.broadcast(unitary=qml.CNOT, wires=self.wires, pattern='ring')
def random_embed(x, wires, n_layers=1):
"""random enbedding circuit
Args:
weights: trainable weights
x: input, len(x) is <= len(wires)
wires: list of wires on which the feature map acts
n_layers: number of repetitions of the first layer (Default value = 1)
Returns:
"""
n_wires = len(wires)
weights = pars_random(x, n_wires, n_layers)
n_weights_needed = n_layers * n_wires
if len(weights) != n_weights_needed:
raise ValueError("Feat map needs {} weights, got {}.".format(
n_weights_needed, len(weights)))
gate_set = [qml.RX, qml.RY, qml.RZ]
for l in range(n_layers):
i = 0
while i < len(x):
gate = np.random.choice(gate_set)
gate(x[i], wires=wires[i])
i = i + 1
for i in range(n_wires):
gate = np.random.choice(gate_set)
gate(weights[l * n_wires + i - n_wires], wires=wires[i])
qml.broadcast(qml.CNOT, wires=range(n_wires), pattern="ring")
def hea(params, wires):
n_qubits = len(wires)
n_rotations = len(params)
if n_rotations > 1:
n_layers = n_rotations // n_qubits
#n_extra_rots = n_rotations - n_layers * n_qubits
# Alternating layers of unitary rotations on every qubit followed by a
# ring cascade of CNOTs.
for layer_idx in range(n_layers):
layer_params = params[layer_idx *
n_qubits:layer_idx * n_qubits +
n_qubits, :]
qml.broadcast(qml.Rot,
wires,
pattern="single",
parameters=layer_params)
qml.broadcast(qml.CNOT, wires, pattern="ring")
# There may be "extra" parameter sets required for which it's not necessarily
# to perform another full alternating cycle. Apply these to the qubits as needed.
#extra_params = params[-n_extra_rots:, :]
#extra_wires = wires[: n_qubits - 1 - n_extra_rots : -1]
#qml.broadcast(qml.Rot, extra_wires, pattern="single", parameters=extra_params)
else:
# For 1-qubit case, just a single rotation to the qubit
qml.Rot(*params[0], wires=wires[0])
def layer(self, features, weights):
for k in range(self.n_qubits):
qml.RY(weights[k] * features[k], wires=self.wires[k])
qml.broadcast(qml.CNOT, wires=self.wires, pattern='ring')
for k in range(self.n_qubits):
qml.RX(weights[self.n_qubits + k], wires=self.wires[k])
def initialize_fields(angles, wires):
qml.broadcast(qml.RX,
wires=wires,
pattern='single',
parameters=np.array(angles)[:, 0])
qml.broadcast(qml.RZ,
wires=wires,
pattern='single',
parameters=np.array(angles)[:, 1])
def evaluate_to_ancillary(params, wires):
unitary_params = np.split(params, len(wires) - 1)
pattern = [[i, wires[-1]] for i in wires[:-1]]
if floq:
for i, used_wires in enumerate(pattern):
CRX(params[i], used_wires)
else:
qml.broadcast(unitary=qml.CRX,
pattern=pattern,
wires=wires,
parameters=unitary_params)
def layer(x, params, wires, i0=0, inc=1):
"""Building block of the embedding Ansatz"""
i = i0
for j, wire in enumerate(wires):
qml.Hadamard(wires=[wire])
qml.RZ(x[i % len(x)], wires=[wire])
i += inc
qml.RY(params[0, j], wires=[wire])
qml.broadcast(unitary=qml.CRZ,
pattern="ring",
wires=wires,
parameters=params[1])
def unitary_layer(all_wires, params, k=2):
"""
Layers of unitaries
The first and last qubits are ancilla qubits
Input:
all_wires: list of wires to be used
params: parameters for unitaries
k: every kth qubit, we apply an aribtrary unitary gate to k+1 qubit
"""
qml.broadcast(ArbitraryUnitary,
wires=all_wires,
pattern="double",
parameters=params)
def circuit(params):
qml.RX(params[0], wires=0)
qml.RY(params[1], wires=1)
qml.RZ(params[2], wires=2)
qml.broadcast(qml.CNOT, wires=[0, 1, 2], pattern="ring")
qml.RX(params[3], wires=0)
qml.RY(params[4], wires=1)
qml.RZ(params[5], wires=2)
qml.broadcast(qml.CNOT, wires=[0, 1, 2], pattern="ring")
return qml.expval(qml.PauliY(0) @ qml.PauliZ(2))
def layer(self, features, weights):
upload = features
if self.angle_scaling:
upload = weights[0:self.n_scaling_weights] * upload
qml.broadcast(unitary=qml.RX,
wires=self.feature_wires,
pattern='single',
parameters=upload)
qml.broadcast(unitary=qml.Hadamard,
wires=self.latent_wires,
pattern='single')
if self.n_total_qubits >= 2:
qml.broadcast(
unitary=qml.MultiRZ,
wires=self.wires,
pattern='ring',
parameters=weights[self.
n_scaling_weights:self.n_scaling_weights +
self.n_entangling_weights])
qml.broadcast(unitary=qml.RY,
wires=self.wires,
pattern='single',
parameters=weights[self.n_scaling_weights +
self.n_entangling_weights:])
def quantum_circuit(rotation_params, coupling_params, sample=None):
# Prepares the initial basis state corresponding to the sample
qml.templates.BasisStatePreparation(sample, wires=range(nr_qubits))
# Prepares the variational ansatz for the circuit
for i in range(0, depth):
single_rotation(rotation_params[i], range(nr_qubits))
qml.broadcast(unitary=qml.CRX,
pattern="ring",
wires=range(nr_qubits),
parameters=coupling_params[i])
# Calculates the expectation value of the Hamiltonian with respect to the prepared states
return qml.expval(qml.Hermitian(ham_matrix, wires=range(nr_qubits)))
def circuit(params1, params2):
"""Parametrized circuit with nearest-neighbour gates."""
for layer in range(n):
qml.broadcast(
qml.RX,
pattern="single",
wires=self.all_wires,
parameters=params1[layer],
)
qml.broadcast(
qml.CRY,
pattern="chain",
wires=self.all_wires,
parameters=params2[layer],
)
return bu.expval(qml.PauliZ(0))
def variational_circuit(params):
"""A layered variational circuit. The first layer comprises of x, y, and z rotations on wires
0, 1, and 2, respectively. The second layer is a ring of CNOT gates. The final layer comprises
of x, y, and z rotations on wires 0, 1, and 2, respectively.
"""
# DO NOT MODIFY anything in this code block
qml.RX(params[0], wires=0)
qml.RY(params[1], wires=1)
qml.RZ(params[2], wires=2)
qml.broadcast(qml.CNOT, wires=[0, 1, 2], pattern="ring")
qml.RX(params[3], wires=0)
qml.RY(params[4], wires=1)
qml.RZ(params[5], wires=2)
qml.broadcast(qml.CNOT, wires=[0, 1, 2], pattern="ring")
def circuit04(params, wires):
"""Circuit template #04 in 1905.10876
Args:
params (array): An array of shapes (3N-1, 1).
wires (Iterable): Wires that the template acts on.
"""
# Input Checks
wires, size = Wires(wires), len(wires)
check_shape(params, target_shape=(3 * size - 1,),
msg=f"params must be of shape {(3 * size - 1,)}.")
# Define the circuit
AngleEmbedding(params[:size], wires, rotation='X')
AngleEmbedding(params[size:2 * size], wires, rotation='Z')
pattern = [[i + 1, i] for i in reversed(range(len(wires) - 1))]
qml.broadcast(unitary=qml.CRX, pattern=pattern, wires=wires, parameters=params[2 * size:])
def variational_ansatz(params, wires, *, state_n):
"""
Args:
params (np.ndarray): An array of floating-point numbers with size (n, 3),
where n is the number of parameter sets required (this is determined by
the problem Hamiltonian).
wires (qml.Wires): The device wires this circuit will run on.
"""
n_qubits = len(wires)
n_rotations = len(params)
state = np.repeat([0], n_qubits)
state[0:state_n] = 1
qml.BasisState(state, wires=wires)
if n_rotations > 1:
n_layers = n_rotations // n_qubits
n_extra_rots = n_rotations - n_layers * n_qubits
# Alternating layers of unitary rotations on every qubit followed by a
# ring cascade of CNOTs.
for layer_idx in range(n_layers):
layer_params = params[layer_idx *
n_qubits: layer_idx * n_qubits + n_qubits, :]
qml.broadcast(qml.RY, wires, pattern="single",
parameters=layer_params[:, 0])
qml.broadcast(qml.RZ, wires, pattern="single",
parameters=layer_params[:, 1])
qml.broadcast(qml.CNOT, wires, pattern="ring")
if n_extra_rots > 0:
# There may be "extra" parameter sets required for which it's not necessarily
# to perform another full alternating cycle. Apply these to the qubits as needed.
extra_params = params[-n_extra_rots:, :]
extra_wires = wires[: n_qubits - 1 - n_extra_rots: -1]
qml.broadcast(qml.RY, extra_wires, pattern="single",
parameters=extra_params[:, 0])
qml.broadcast(qml.RZ, extra_wires, pattern="single",
parameters=extra_params[:, 1])
else:
# For 1-qubit case, just a single rotation to the qubit
qml.Rot(*params[0], wires=wires[0])
def random_embed(weights, x, wires, n_layers=1):
""" random enbedding circuit
:param weights: trainable weights
:param x: input, len(x) is <= len(wires)
:param wires: list of wires on which the feature map acts
:param n_layers: number of repetitions of the first layer
"""
n_wires = len(wires)
n_weights_needed = n_layers * n_wires
gate_set = [qml.RX, qml.RY, qml.RZ]
for l in range(n_layers):
i = 0
while i < len(x):
gate = np.random.choice(gate_set)
gate(x[i], wires=wires[i])
i = i + 1
for i in range(n_wires):
gate = np.random.choice(gate_set)
gate(weights[l * n_wires + i - n_wires], wires=wires[i])
qml.broadcast(qml.CNOT, wires=range(n_wires), pattern="ring")
def circuit09(params, wires):
"""Circuit template #09 in 1905.10876
Args:
params (array): An array of shapes (N,).
wires (Iterable): Wires that the template acts on.
"""
# Input Checks
wires, size = Wires(wires), len(wires)
check_shape(params, target_shape=(size,),
msg=f"params must be of shape {(size,)}.")
# Define the circuit
qml.broadcast(unitary=qml.Hadamard, pattern="single", wires=wires)
pattern = [[i + 1, i] for i in reversed(range(len(wires) - 1))]
qml.broadcast(unitary=qml.CZ, pattern=pattern, wires=wires)
AngleEmbedding(params, wires, rotation='X')
def classifier(params, wires):
# qml.RZ(params, wires=wires[0])
# qml.RY(params, wires=wires[0])
n_qubits = len(wires)
n_rotations = len(params)
n_layers = n_rotations // n_qubits
if n_rotations % n_qubits != 0:
raise Exception("Last layer is incomplete, not all qubits are rotated")
# Alternating layers of unitary rotations on every qubit followed by a
# ring cascade of CNOTs.
for layer_idx in range(n_layers):
layer_params = params[layer_idx * n_qubits:layer_idx * n_qubits +
n_qubits, :]
qml.broadcast(qml.Rot,
wires,
pattern="single",
parameters=layer_params)
if n_qubits > 1:
qml.broadcast(qml.CNOT, wires, pattern="ring")
def variational_ansatz2(params, wires):
rot = params[0]
params = params[1:]
n_qubits = len(wires)
n_rotations = len(params)
qml.PauliX(wires=0)
qml.PauliX(wires=1)
qml.RY(rot[2], wires=2)
if n_rotations > 1:
n_layers = n_rotations // n_qubits
n_extra_rots = n_rotations - n_layers * n_qubits
for layer_idx in range(n_layers):
layer_params = params[layer_idx *
n_qubits:layer_idx * n_qubits +
n_qubits, :]
qml.broadcast(qml.Rot,
wires,
pattern="single",
parameters=layer_params)
qml.broadcast(qml.CNOT, wires, pattern="ring")
extra_params = params[-n_extra_rots:, :]
extra_wires = wires[:n_qubits - 1 - n_extra_rots:-1]
qml.broadcast(qml.Rot,
extra_wires,
pattern="single",
parameters=extra_params)
else:
qml.Rot(*params[0], wires=wires[0])
def variational_ansatz(params, wires):
"""
This is a custom ansatz. It applies alternating layers of rotations and CNOTs.
Args:
params (np.ndarray): An array of floating-point numbers with size (n, 3),
where n is the number of parameter sets required (this is determined by
the problem Hamiltonian).
wires (qml.Wires): The device wires this circuit will run on.
"""
n_qubits = len(wires)
n_rotations = len(params)
if n_rotations > 1:
n_layers = n_rotations // n_qubits
n_extra_rots = n_rotations - n_layers * n_qubits
# Alternating layers of unitary rotations on every qubit followed by a
# ring cascade of CNOTs.
for layer_idx in range(n_layers):
layer_params = params[layer_idx * n_qubits : layer_idx * n_qubits + n_qubits, :]
qml.broadcast(qml.Rot, wires, pattern="single", parameters=layer_params)
qml.broadcast(qml.CNOT, wires, pattern="ring")
extra_params = params[-n_extra_rots:, :]
extra_wires = wires[: n_qubits - 1 - n_extra_rots : -1]
qml.broadcast(qml.Rot, extra_wires, pattern="single", parameters=extra_params)
else:
# For 1-qubit case, just a single rotation to the qubit
qml.Rot(*params[0], wires=wires[0])
def variational_ansatz(params, wires):
"""
DO NOT MODIFY anything in this function! It is used to judge your solution.
This is ansatz is used to help with the problem structure. It applies
alternating layers of rotations and CNOTs.
Don't worry about the contents of this function for now—you'll be designing
your own ansatze in a later problem.
Args:
params (np.ndarray): An array of floating-point numbers with size (n, 3),
where n is the number of parameter sets required (this is determined by
the problem Hamiltonian).
wires (qml.Wires): The device wires this circuit will run on.
"""
n_qubits = len(wires)
n_rotations = len(params)
if n_rotations > 1:
n_layers = n_rotations // n_qubits
n_extra_rots = n_rotations - n_layers * n_qubits
# Alternating layers of unitary rotations on every qubit followed by a
# ring cascade of CNOTs.
for layer_idx in range(n_layers):
layer_params = params[layer_idx * n_qubits : layer_idx * n_qubits + n_qubits, :]
qml.broadcast(qml.Rot, wires, pattern="single", parameters=layer_params)
qml.broadcast(qml.CNOT, wires, pattern="ring")
# There may be "extra" parameter sets required for which it's not necessarily
# to perform another full alternating cycle. Apply these to the qubits as needed.
extra_params = params[-n_extra_rots:, :]
extra_wires = wires[: n_qubits - 1 - n_extra_rots : -1]
qml.broadcast(qml.Rot, extra_wires, pattern="single", parameters=extra_params)
else:
# For 1-qubit case, just a single rotation to the qubit
qml.Rot(*params[0], wires=wires[0])
def generator(w):
qml.broadcast(unitary=qml.RY,
pattern='single',
wires=wires,
parameters=w[0:32])
for k in range(1, 4):
qml.broadcast(unitary=qml.RY,
pattern='single',
wires=wires,
parameters=w[(32 * k):(32 * (k + 1))])
qml.broadcast(unitary=qml.CZ, pattern='ring', wires=wires)
def get_state(params, wires=w2):
n_qubits = len(wires)
n_rotations = len(params)
if n_rotations > 1:
n_layers = n_rotations // n_qubits
n_extra_rots = n_rotations - n_layers * n_qubits
for layer_idx in range(n_layers):
layer_params = params[layer_idx * n_qubits : layer_idx * n_qubits + n_qubits, :]
qml.broadcast(qml.Rot, wires, pattern="single", parameters=layer_params)
qml.broadcast(qml.CNOT, wires, pattern="ring")
extra_params = params[-n_extra_rots:, :]
extra_wires = wires[: n_qubits - 1 - n_extra_rots : -1]
qml.broadcast(qml.Rot, extra_wires, pattern="single", parameters=extra_params)
else:
qml.Rot(*params[0], wires=wires[0])
return qml.state()
