def __init__(self, q_device: tq.QuantumDevice = None):
super().__init__()
self.q_device = q_device
self.n_gate = 10
self.gate0 = tq.CNOT()
# self.gate1 = tq.CNOT()
self.submodules = tq.QuantumModuleList()
self.q_layer0 = TQAll(self.n_gate, tq.CNOT)
for k in range(self.n_gate):
self.submodules.append(tq.RY())
# for k in range(self.n_gate):
# self.submodules.append(tq.CNOT())
# self.gate0 = tq.RY(has_params=False, trainable=False)
# self.gate1 = tq.RX(has_params=False, trainable=False)
# self.gate2 = tq.RZ(has_params=False, trainable=False)
self.gate1 = tq.RX(has_params=True, trainable=True)
self.gate2 = tq.RZ(has_params=True, trainable=True)
self.gate3 = tq.RY(has_params=True, trainable=True)
# self.gate3 = tq.CNOT()
self.gate4 = tq.RX(has_params=True, trainable=True)
self.gate5 = tq.RZ(has_params=True, trainable=True)
self.gate6 = tq.RY(has_params=True, trainable=True)
self.gate7 = tq.RX()
self.gate8 = tq.U2(has_params=True, trainable=True)
self.gate9 = tq.TrainableUnitary(has_params=True,
trainable=True,
n_wires=3)
self.gate10 = tq.MultiXCNOT(n_wires=5)
self.gate11 = tq.MultiCNOT(n_wires=3)
def __init__(self):
super().__init__()
self.n_wires = 4
self.random_layer = tq.RandomLayer(n_ops=50,
wires=list(range(self.n_wires)))
# gates with trainable parameters
self.rx0 = tq.RX(has_params=True, trainable=True)
self.ry0 = tq.RY(has_params=True, trainable=True)
self.rz0 = tq.RZ(has_params=True, trainable=True)
self.crx0 = tq.CRX(has_params=True, trainable=True)
def __init__(self):
super().__init__()
self.n_gate = 12
self.gate0 = tq.CNOT()
self.gate1 = tq.CNOT()
self.submodules = tq.QuantumModuleList()
self.q_layer0 = TQAll(self.n_gate, tq.CNOT)
for k in range(self.n_gate):
self.submodules.append(tq.RY())
# for k in range(self.n_gate):
# self.submodules.append(tq.CNOT())
# self.gate0 = tq.RY(has_params=False, trainable=False)
# self.gate1 = tq.RX(has_params=False, trainable=False)
# self.gate2 = tq.RZ(has_params=False, trainable=False)
self.gate1 = tq.RX(has_params=True, trainable=True)
self.gate2 = tq.RZ(has_params=True, trainable=True)
self.gate3 = tq.RY(has_params=True, trainable=True)
# self.gate3 = tq.CNOT()
self.gate4 = tq.RX(has_params=True, trainable=True)
self.gate5 = tq.RZ(has_params=True, trainable=True)
self.gate6 = tq.RY(has_params=True, trainable=True)
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2 = nn.Conv2d(32, 64, 3, 1)
self.dropout1 = nn.Dropout(0.25)
self.dropout2 = nn.Dropout(0.5)
self.fc1 = nn.Linear(9216, 128)
self.fc2 = nn.Linear(128, 10)
self.sigmoid = nn.Sigmoid()
self.q_device0 = tq.QuantumDevice(n_wires=12)
self.q_device0_1 = tq.QuantumDevice(n_wires=10)
self.q_layer0 = tq.TrainableOpAll(n_gate=12, op=tq.RX)
# self.q_layer1 = OpAll(n_gate=10, op=tq.RY)
self.q_layer2 = tq.RX(has_params=True,
trainable=False,
init_params=-np.pi / 4)
self.q_layer3 = tq.RZ(has_params=True, trainable=True)
self.q_device1 = tq.QuantumDevice(n_wires=3)
self.q_layer4 = tq.CY()
self.q_layer5 = tq.Toffoli()
self.q_layer6 = tq.PhaseShift(has_params=True, trainable=True)
self.q_layer7 = tq.Rot(has_params=True, trainable=True)
self.q_layer8 = tq.MultiRZ(has_params=True, trainable=True, n_wires=5)
self.q_layer9 = tq.CRX(has_params=True, trainable=True)
self.q_layer10 = tq.CRY(has_params=True, trainable=True)
self.q_layer11 = tq.CRZ(has_params=True, trainable=True)
self.q_layer12 = tq.CRot(
has_params=True,
trainable=False,
init_params=[-np.pi / 4, np.pi / 4, np.pi / 2])
self.q_layer13 = tq.U1(has_params=True,
trainable=False,
init_params=np.pi / 7)
self.q_layer14 = tq.U2(has_params=True,
trainable=True,
init_params=[np.pi / 7, np.pi / 8.8])
self.q_layer15 = tq.U3(has_params=True, trainable=True)
self.q_layer16 = tq.QubitUnitary(has_params=True,
trainable=False,
init_params=[[1, 0], [0, 1]])
self.q_test_layer = TestModule()
self.random_layer = tq.RandomLayer(30, wires=[0, 3, 5, 7])
# self.random_layer.static_on(wires_per_block=3)
# self.q_test_layer.static_on(wires_per_block=4)
self.q_device2 = tq.QuantumDevice(n_wires=3)
self.t00 = T00()
def __init__(self):
super().__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2 = nn.Conv2d(32, 64, 3, 1)
self.dropout1 = nn.Dropout(0.25)
self.dropout2 = nn.Dropout(0.5)
self.fc1 = nn.Linear(9216, 128)
self.fc2 = nn.Linear(128, 10)
self.sigmoid = nn.Sigmoid()
self.q_device0 = tq.QuantumDevice(n_wires=10)
self.q_layer0 = tq.TrainableOpAll(n_gate=10, op=tq.RX)
self.q_layer1 = tq.ClassicalInOpAll(n_gate=10, op=tq.RX)
self.q_layer2 = tq.RX(has_params=True,
trainable=False,
init_params=-np.pi / 4)
self.q_layer3 = tq.RZ(has_params=True, trainable=True)
self.q_device1 = tq.QuantumDevice(n_wires=3)
self.q_layer4 = tq.CY()
self.q_layer5 = tq.Toffoli()
self.q_layer6 = tq.PhaseShift(has_params=True, trainable=True)
self.q_layer7 = tq.Rot(has_params=True, trainable=True)
self.q_layer8 = tq.MultiRZ(has_params=True, trainable=True, n_wires=5)
self.q_layer9 = tq.CRX(has_params=True, trainable=True)
self.q_layer10 = tq.CRY(has_params=True, trainable=True)
self.q_layer11 = tq.CRZ(has_params=True, trainable=True)
self.q_layer12 = tq.CRot(
has_params=True,
trainable=False,
init_params=[-np.pi / 4, np.pi / 4, np.pi / 2])
self.q_layer13 = tq.U1(has_params=True,
trainable=False,
init_params=np.pi / 7)
self.q_layer14 = tq.U2(has_params=True,
trainable=True,
init_params=[np.pi / 7, np.pi / 8.8])
self.q_layer15 = tq.U3(has_params=True, trainable=True)
self.q_layer16 = tq.QubitUnitary(has_params=True,
trainable=False,
init_params=[[1, 0], [0, 1]])
self.q_layer17 = tq.MultiCNOT(n_wires=5)
self.q_layer18 = tq.MultiXCNOT(n_wires=5)
def __init__(self, n_wires):
super().__init__()
self.n_wires = n_wires
self.gate1 = tq.RX(has_params=True, trainable=True)
self.gate2 = tq.RY(has_params=True, trainable=True)
self.gate3 = tq.RZ(has_params=True, trainable=True)