Why is the quantum device in the colab notebook of Quanvolution example reused so many times?

[hifi-hyp]

In each iteration, the state of the self.q_deivce will be changed. Why is the changed self.q_device in the previous iteration used in the next iteration here?

class QuanvolutionFilter(tq.QuantumModule):
    def __init__(self):
        super().__init__()
        self.n_wires = 4
        self.q_device = tq.QuantumDevice(n_wires=self.n_wires)
        self.encoder = tq.GeneralEncoder(
        [   {'input_idx': [0], 'func': 'ry', 'wires': [0]},
            {'input_idx': [1], 'func': 'ry', 'wires': [1]},
            {'input_idx': [2], 'func': 'ry', 'wires': [2]},
            {'input_idx': [3], 'func': 'ry', 'wires': [3]},])

        self.q_layer = tq.RandomLayer(n_ops=8, wires=list(range(self.n_wires)))
        self.measure = tq.MeasureAll(tq.PauliZ)

    def forward(self, x, use_qiskit=False):
        bsz = x.shape[0]
        size = 28
        x = x.view(bsz, size, size)

        data_list = []

        for c in range(0, size, 2):
            for r in range(0, size, 2):
                data = torch.transpose(torch.cat((x[:, c, r], x[:, c, r+1], x[:, c+1, r], x[:, c+1, r+1])).view(4, bsz), 0, 1)
                if use_qiskit:
                    data = self.qiskit_processor.process_parameterized(
                        self.q_device, self.encoder, self.q_layer, self.measure, data)
                else:
                    self.encoder(self.q_device, data)
                    self.q_layer(self.q_device)
                    data = self.measure(self.q_device)

                data_list.append(data.view(bsz, 4))

        result = torch.cat(data_list, dim=1).float()

        return result

[Hanrui-Wang]

Hi hifi-hyp,

The newly suggested way to use quantum device is to initialize it in forward like below: I will update the notebook soon. You may have a look at python script. Thanks for the feedback!

class QuanvolutionFilter(tq.QuantumModule):
    def __init__(self):
        super().__init__()
        self.n_wires = 4
        self.encoder = tq.GeneralEncoder(
            [
                {"input_idx": [0], "func": "ry", "wires": [0]},
                {"input_idx": [1], "func": "ry", "wires": [1]},
                {"input_idx": [2], "func": "ry", "wires": [2]},
                {"input_idx": [3], "func": "ry", "wires": [3]},
            ]
        )

        self.q_layer = tq.RandomLayer(n_ops=8, wires=list(range(self.n_wires)))
        self.measure = tq.MeasureAll(tq.PauliZ)

    def forward(self, x, use_qiskit=False):
        bsz = x.shape[0]
        qdev = tq.QuantumDevice(self.n_wires, bsz=bsz, device=x.device)
        size = 28
        x = x.view(bsz, size, size)

        data_list = []

        for c in range(0, size, 2):
            for r in range(0, size, 2):
                data = torch.transpose(
                    torch.cat(
                        (x[:, c, r], x[:, c, r + 1], x[:, c + 1, r], x[:, c + 1, r + 1])
                    ).view(4, bsz),
                    0,
                    1,
                )
                if use_qiskit:
                    data = self.qiskit_processor.process_parameterized(
                        qdev, self.encoder, self.q_layer, self.measure, data
                    )
                else:
                    self.encoder(qdev, data)
                    self.q_layer(qdev)
                    data = self.measure(qdev)

                data_list.append(data.view(bsz, 4))

        result = torch.cat(data_list, dim=1).float()

        return result

[hifi-hyp]

Hi @Hanrui-Wang , thanks for your reply! However, after I use the updated code you provide, I still cannot reproduce the results for Quanvolution Neural Network. The test accuracy is only around 32% after 15-epoch training.

[hifi-hyp]

Here is my training code, which is the same as the notebook you provide.

import torch
import torch.nn.functional as F
import torch.optim as optim
import numpy as np

import torchquantum as tq
import random

from torchquantum.datasets import MNIST
from torch.optim.lr_scheduler import CosineAnnealingLR

class QuanvolutionFilter(tq.QuantumModule):
    def __init__(self):
        super().__init__()
        self.n_wires = 4
        self.encoder = tq.GeneralEncoder(
            [
                {"input_idx": [0], "func": "ry", "wires": [0]},
                {"input_idx": [1], "func": "ry", "wires": [1]},
                {"input_idx": [2], "func": "ry", "wires": [2]},
                {"input_idx": [3], "func": "ry", "wires": [3]},
            ]
        )

        self.q_layer = tq.RandomLayer(n_ops=8, wires=list(range(self.n_wires)))
        self.measure = tq.MeasureAll(tq.PauliZ)

    def forward(self, x, use_qiskit=False):
        bsz = x.shape[0]
        qdev = tq.QuantumDevice(self.n_wires, bsz=bsz, device=x.device)
        size = 28
        x = x.view(bsz, size, size)

        data_list = []

        for c in range(0, size, 2):
            for r in range(0, size, 2):
                data = torch.transpose(
                    torch.cat(
                        (x[:, c, r], x[:, c, r + 1], x[:, c + 1, r], x[:, c + 1, r + 1])
                    ).view(4, bsz),
                    0,
                    1,
                )
                if use_qiskit:
                    data = self.qiskit_processor.process_parameterized(
                        qdev, self.encoder, self.q_layer, self.measure, data
                    )
                else:
                    self.encoder(qdev, data)
                    self.q_layer(qdev)
                    data = self.measure(qdev)

                data_list.append(data.view(bsz, 4))

        result = torch.cat(data_list, dim=1).float()

        return result

class HybridModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.qf = QuanvolutionFilter()
        self.linear = torch.nn.Linear(4*14*14, 10)

    def forward(self, x, use_qiskit=False):
        with torch.no_grad():
          x = self.qf(x, use_qiskit)
        x = self.linear(x)
        return F.log_softmax(x, -1)

class HybridModel_without_qf(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = torch.nn.Linear(28*28, 10)

    def forward(self, x, use_qiskit=False):
        x = x.view(-1, 28*28)
        x = self.linear(x)
        return F.log_softmax(x, -1)

random.seed(42)
np.random.seed(42)
torch.manual_seed(42)
dataset = MNIST(
    root='./mnist_data',
    train_valid_split_ratio=[0.9, 0.1],
    n_test_samples=300,
    n_train_samples=500,
)
dataflow = dict()

for split in dataset:
    sampler = torch.utils.data.RandomSampler(dataset[split])
    dataflow[split] = torch.utils.data.DataLoader(
        dataset[split],
        batch_size=10,
        sampler=sampler,
        num_workers=8,
        pin_memory=True)

use_cuda = torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
model = HybridModel().to(device)
model_without_qf = HybridModel_without_qf().to(device)
n_epochs = 15
optimizer = optim.Adam(model.parameters(), lr=5e-3, weight_decay=1e-4)
scheduler = CosineAnnealingLR(optimizer, T_max=n_epochs)

accu_list1 = []
loss_list1 = []
accu_list2 = []
loss_list2 = []

def train(dataflow, model, device, optimizer):
    for feed_dict in dataflow['train']:
        inputs = feed_dict['image'].to(device)
        targets = feed_dict['digit'].to(device)

        outputs = model(inputs)
        loss = F.nll_loss(outputs, targets)
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()
        print(f"loss: {loss.item()}", end='\r')

def valid_test(dataflow, split, model, device, qiskit=False):
    target_all = []
    output_all = []
    with torch.no_grad():
        for feed_dict in dataflow[split]:
            inputs = feed_dict['image'].to(device)
            targets = feed_dict['digit'].to(device)

            outputs = model(inputs, use_qiskit=qiskit)

            target_all.append(targets)
            output_all.append(outputs)
        target_all = torch.cat(target_all, dim=0)
        output_all = torch.cat(output_all, dim=0)

    _, indices = output_all.topk(1, dim=1)
    masks = indices.eq(target_all.view(-1, 1).expand_as(indices))
    size = target_all.shape[0]
    corrects = masks.sum().item()
    accuracy = corrects / size
    loss = F.nll_loss(output_all, target_all).item()

    print(f"{split} set accuracy: {accuracy}")
    print(f"{split} set loss: {loss}")

    return accuracy, loss

for epoch in range(1, n_epochs + 1):
    # train
    print(f"Epoch {epoch}:")
    train(dataflow, model, device, optimizer)
    print(optimizer.param_groups[0]['lr'])

    # valid
    accu, loss = valid_test(dataflow, 'test', model, device, )
    accu_list1.append(accu)
    loss_list1.append(loss)
    scheduler.step()

[waileuklo]

@hifi-hyp Try initializing the q_device within the double loop and lose the torch.no_grad() in the forward method of the class HybridModel:

for c in range(0, size, 2):
     for r in range(0, size, 2):
        qdev = tq.QuantumDevice(self.n_wires, bsz=bsz, device=x.device)

class HybridModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.qf = QuanvolutionFilter()
        self.linear = torch.nn.Linear(4*14*14, 10)

    def forward(self, x, use_qiskit=False):
        x = self.qf(x, use_qiskit)
        x = self.linear(x)
        return F.log_softmax(x, -1)

[BaophanN]

initializing the q_device within the double loop and lose the torch.no_grad() in the forward method of the class HybridModel:

Can you explain why doing this can increase the accuracy on the test set?

[waileuklo]

initializing the q_device within the double loop and lose the torch.no_grad() in the forward method of the class HybridModel:

Can you explain why doing this can increase the accuracy on the test set?

The initiated quantum device is the leaf of backward propagation, i.e. the propagation ends at it. Thus, it should be put at the same level of the layers applied on it, the layers being self.encoder(qdev, data), self.q_layer(qdev) and data = self.measure(qdev). If initiated outside the double loops, the backward propagation would give an error as we would be trying to propagate multiple times while the computational graph created by pytorch is deleted after one propagation by design.

Losing torch.no_grad() is just so that we are able to do backward propagation.