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MERA1.py
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MERA1.py
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import pennylane as qml
from pennylane import numpy as np
import tensorflow as tf
import pennylane_qulacs
import os
##################################################################################################
# Use default.qubit for default pennylane simulation
# use tf.interface for TF integration
USE_GPU = (os.environ["CUDA_VISIBLE_DEVICES"] != '-1')
dev1 = qml.device("qulacs.simulator", wires=8, gpu=USE_GPU)
@qml.qnode(dev1,interface='tf')
def TTN_edge_forward(edge_array,theta_learn):
# Takes the input and learning variables and applies the
# network to obtain the output
# STATE PREPARATION
for i in range(8):
qml.RY(edge_array[i],wires=i)
# First Layer
qml.RY(theta_learn[0],wires=1)
qml.RY(theta_learn[1],wires=2)
qml.CNOT(wires=[1,2])
qml.RY(theta_learn[2],wires=5)
qml.RY(theta_learn[3],wires=6)
qml.CNOT(wires=[6,5])
# Second Layer
qml.RY(theta_learn[4],wires=0)
qml.RY(theta_learn[5],wires=1)
qml.CNOT(wires=[0,1])
qml.RY(theta_learn[6],wires=2)
qml.RY(theta_learn[7],wires=3)
qml.CNOT(wires=[3,2])
qml.RY(theta_learn[8],wires=4)
qml.RY(theta_learn[9],wires=5)
qml.CNOT(wires=[4,5])
qml.RY(theta_learn[10],wires=6)
qml.RY(theta_learn[11],wires=7)
qml.CNOT(wires=[7,6])
# Third Layer
qml.RY(theta_learn[12],wires=2)
qml.RY(theta_learn[13],wires=5)
qml.CNOT(wires=[2,5])
# Fifth Layer
qml.RY(theta_learn[14],wires=1)
qml.RY(theta_learn[15],wires=2)
qml.CNOT(wires=[1,2])
qml.RY(theta_learn[16],wires=5)
qml.RY(theta_learn[17],wires=6)
qml.CNOT(wires=[6,5])
# Third Layer
qml.RY(theta_learn[18],wires=2)
qml.RY(theta_learn[19],wires=5)
qml.CNOT(wires=[2,5])
qml.RY(theta_learn[20],wires=5)
# return expectation value of the circuit
return qml.expval(qml.PauliZ(wires=5))
##################################################################################################
# Use default.qubit for default pennylane simulation
# use tf.interface for TF integration
dev2 = qml.device("qulacs.simulator", wires=12, gpu=USE_GPU)
@qml.qnode(dev2,interface='tf')
def TTN_node_forward(node_array,theta_learn):
# Takes the input and learning variables and applies the
# network to obtain the output
# STATE PREPARATION
for i in range(12):
qml.RY(node_array[i],wires=i)
# First Layer
qml.RY(theta_learn[0],wires=1)
qml.RY(theta_learn[1],wires=2)
qml.CNOT(wires=[1,2])
qml.RY(theta_learn[2],wires=3)
qml.RY(theta_learn[3],wires=4)
qml.CNOT(wires=[4,3])
qml.RY(theta_learn[4],wires=5)
qml.RY(theta_learn[5],wires=6)
qml.CNOT(wires=[5,6])
qml.RY(theta_learn[6],wires=7)
qml.RY(theta_learn[7],wires=8)
qml.CNOT(wires=[7,8])
qml.RY(theta_learn[8],wires=9)
qml.RY(theta_learn[9],wires=10)
qml.CNOT(wires=[10,9])
# Second Layer
qml.RY(theta_learn[10],wires=0)
qml.RY(theta_learn[11],wires=1)
qml.CNOT(wires=[0,1])
qml.RY(theta_learn[12],wires=2)
qml.RY(theta_learn[13],wires=3)
qml.CNOT(wires=[3,2])
qml.RY(theta_learn[14],wires=4)
qml.RY(theta_learn[15],wires=5)
qml.CNOT(wires=[4,5])
qml.RY(theta_learn[16],wires=6)
qml.RY(theta_learn[17],wires=7)
qml.CNOT(wires=[7,6])
qml.RY(theta_learn[18],wires=8)
qml.RY(theta_learn[19],wires=9)
qml.CNOT(wires=[8,9])
qml.RY(theta_learn[20],wires=10)
qml.RY(theta_learn[21],wires=11)
qml.CNOT(wires=[11,10])
# Third Layer
qml.RY(theta_learn[22],wires=2)
qml.RY(theta_learn[23],wires=5)
qml.CNOT(wires=[5,2])
qml.RY(theta_learn[24],wires=6)
qml.RY(theta_learn[25],wires=9)
qml.CNOT(wires=[6,9])
# Fourth Layer
qml.RY(theta_learn[26],wires=1)
qml.RY(theta_learn[27],wires=2)
qml.CNOT(wires=[1,2])
qml.RY(theta_learn[28],wires=9)
qml.RY(theta_learn[29],wires=10)
qml.CNOT(wires=[10,9])
# Fifth Layer
qml.RY(theta_learn[30],wires=2)
qml.RY(theta_learn[31],wires=9)
qml.CNOT(wires=[2,9])
# Last Layer
qml.RY(theta_learn[32],wires=9)
# return expectation value of the circuit
return qml.expval(qml.PauliZ(wires=9))
##################################################################################################
def edge_forward(edge_array,theta_learn):
# executes TTN_edge circuit for each edge in edge_array
# To Do: can parallize the for loop
outputs = []
for i in range(len(edge_array[:,0])):
out = tf.constant((1-TTN_edge_forward(edge_array[i,:],theta_learn))/2.,dtype=tf.float64)
outputs.append(out)
return tf.stack(outputs) # output is between [0,1]
##################################################################################################
def node_forward(node_array,theta_learn):
# executes TTN_node circuit for each node in node_array
# To Do: can parallize the for loop
outputs = []
for i in range(len(node_array[:,0])):
out = tf.constant(np.pi*(1-TTN_node_forward(node_array[i,:],theta_learn))/2.,dtype=tf.float64)
outputs.append(out)
return tf.stack(outputs) # output is between [0,2*pi]
##################################################################################################
class EdgeNet(tf.keras.layers.Layer):
def __init__(self, config, name='EdgeNet'):
super(EdgeNet, self).__init__(name=name)
# can only work with hid_dim = 1
# read parameters of the network from file
# params are created using tools/init_params.py
#self.theta_learn = tf.Variable(get_params('EN',config)[0])
self.theta_learn = tf.Variable(tf.random.uniform(shape=[21,],minval=0,maxval=np.pi*4,dtype=tf.float64))
def call(self,X, Ri, Ro):
bo = tf.matmul(Ro,X,transpose_a=True)
bi = tf.matmul(Ri,X,transpose_a=True)
# Shape of B = N_edges x 6 (2x (3 coordinates))
# each row consists of two node that are possibly connected.
B = tf.concat([bo, bi], axis=1)
return edge_forward(B,self.theta_learn)
##################################################################################################
class NodeNet(tf.keras.layers.Layer):
def __init__(self, config, name='NodeNet'):
super(NodeNet, self).__init__(name=name)
# can only work with hid_dim = 1
# read parameters of the network from file
# params are created using tools/init_params.py
#self.theta_learn = tf.Variable(get_params('NN',config)[0])
self.theta_learn = tf.Variable(tf.random.uniform(shape=[33,],minval=0,maxval=np.pi*4,dtype=tf.float64))
def call(self, X, e, Ri, Ro):
bo = tf.matmul(Ro, X, transpose_a=True)
bi = tf.matmul(Ri, X, transpose_a=True)
Rwo = tf.math.multiply(Ro,e)
Rwi = tf.math.multiply(Ri,e)
mi = tf.matmul(Rwi, bo)
mo = tf.matmul(Rwo, bi)
# Shape of M = N_nodes x 9 (3x (3 coordinates))
# each row consists of a node and its 2 possible neigbours
M = tf.concat([mi, mo, X], axis=1)
return node_forward(M,self.theta_learn)
##################################################################################################
class InputNet(tf.keras.layers.Layer):
def __init__(self, config, name):
super(InputNet, self).__init__(name=name)
self.num_outputs = config['hid_dim'] # num_outputs = number of hidden dimensions
# read parameters of the network from file
# params are created using tools/init_params.py
#init = tf.constant_initializer(get_params('IN',config)[0])
# setup a Dense layer with the given config
self.layer = tf.keras.layers.Dense(self.num_outputs,input_shape=(3,),activation='sigmoid')
def call(self, arr):
return self.layer(arr)*np.pi # to map to output to [0,2*pi]
##################################################################################################
class GNN(tf.keras.Model):
def __init__(self, config):
# Network definitions here
super(GNN, self).__init__(name='GNN')
self.InputNet = InputNet(config = config, name='InputNet')
self.EdgeNet = EdgeNet(config = config, name='EdgeNet')
self.NodeNet = NodeNet(config = config, name='NodeNet')
self.n_iters = config['n_iters']
def call(self, graph_array):
X,Ri,Ro = graph_array # decompose the graph array
H = self.InputNet(X) # execute InputNet to produce hidden dimensions
H = tf.concat([H,X],axis=1) # add new dimensions to original X matrix
for i in range(self.n_iters): # recurrent iteration of the network
e = self.EdgeNet(H, Ri, Ro) # execute EdgeNet
H = self.NodeNet(H, e, Ri, Ro) # execute NodeNet using the output of EdgeNet
H = tf.concat([H[:,None],X],axis=1) # update H with the output of NodeNet
e = self.EdgeNet(H, Ri, Ro) # execute EdgeNet one more time to obtain edge predictions
return e # return edge prediction array
##################################################################################################