Parameter Broadcasting Extension

PennyLane Help
1

Recently I found that Parameter Broadcasting is one of the most useful things while running the QML method with a huge batch of data, and I wondered whether I could apply Parameter Broadcasting on two different dimensions (or called batch size for input_feature).

For instance, I have a simple Quantum Circuit:

import pennylane as qml
import numpy as np

input_qubit = 4

dev = qml.device("default.qubit", wires=input_qubit)
@qml.qnode(dev)
def circuit(input_feature, weights):
    qml.AmplitudeEmbedding(features=input_feature, wires=range(input_qubit), normalize=True, pad_with=0.)
    qml.RY(weights[0], wires=0)
    qml.CNOT(wires=[0,1])
    qml.RY(weights[1], wires=0)
    return qml.probs(wires=range(input_qubit))

And the data shape is given like this:

input_size = 2**input_qubit
input_batch = 500

ry_size = 2
ry_dim = 10


# input_feature: (input_batch, input_size)
input_feature = np.random.rand(input_batch,input_size) 

# ry_weight: (ry_size, ry_dim)
ry_weight = np.random.rand(ry_size, ry_dim)

Next, I apply Parameter Broadcasting depending on input_batch(the input dimension) / ry_dim related to the function method_1() / method_2() below. From these two methods, we can compute each ry_dim quantum circuit output result for all input_feature in the batch, then sum the whole ry_dim result for each input_feature, and we will have the final_output with dim=(batch_size, input_size).

We can get the same result from these two methods. And the question is, could I apply Parameter Broadcasting on two different dimensions without using for loop in the function?

def method_1():
    for ry_index in range(ry_dim):

        # output_tmp: (input_batch, input_size)
        output_tmp = circuit(input_feature, ry_weight[:,ry_index])


        # final_output: (input_batch, input_size)
        if ry_index == 0:                    
            final_output = output_tmp    # for the first time
        else: 
            final_output = final_output + output_tmp


    return final_output



def method_2():
    for input_index in range(input_batch):

        # output_tmp_1: (ry_dim, input_size)
        output_tmp_1 = circuit(input_feature[input_index], ry_weight)

        # output_tmp_2: (input_size)
        output_tmp_2 = np.sum(output_tmp_1, axis=0)


        # final_output: (input_batch, input_size)
        if input_index == 0:                    
            final_output = np.expand_dims(output_tmp_2, axis=0) # for the first time
        else: 
            output_tmp_2 = np.expand_dims(output_tmp_2, axis=0)
            final_output = np.concatenate((final_output, output_tmp_2))


    return final_output




method_1_output = method_1()
method_2_output = method_2()

print(method_1_output.shape)
print(method_2_output.shape)

print((method_1_output==method_2_output).all())
2

Hi @mini ,

Thank you for your question.

I don’t really understand your method 1 and method 2 but if I understand correctly you want to get the output of your circuit for every set of input features in your ‘inputs batch’ and for each of these sets of features you want to try 10 different sets of weights.

If this is what you need then I cannot think of any option without using a For loop.
Is there a specific reason why you prefer not to have the For loop? If you try the following code it will give you a list with shape (500, 10, 16) where you will have the 16 probabilities for the 10 weight pairs and for the 500 sets of input features. Then you can work with each of these outputs in the way that best suits your problem.

method_0_output = [circuit(input_feature[i], ry_weight) for i in range(input_batch)]

print(np.shape(method_0_output))

Please let me know if these helps!