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Quantum computing is seen as a new solution to algorithmic problems that are difficult to solve because of these advantages. Various studies using quantum computing models are also being conducted in the field of machine learning. Furthermore, since the optimization of quantum devices using the gradient descent method has been studied, It is possible to learn quantum machine learning using hyperparameters quickly.

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Convolutional Neural Networks have the limitation that they learn inefficiently if the data or model dimension is very large.So,Seunghyeok Oh et al.showed how to make ...

Through this article, we have seen how QCNN uses a CNN model and a quantum computing environment to enable a variety of approaches in the field. Fully parameterized quantum convolutional neural networks open up promising results for quantum machine learning and data science applications. Apart fr...

One of the most popular applications of CNN is in the field of image classification. In terms of superposition and parallel computation, quantum computers offer significant advantages. Quantum Convolutional Neural Network improves CNN performance by incorporating quantum environments. In this sec...

  1. The hidden state is discovered by applying multiple qubit gates between adjacent qubits in the convolution circuit.
  2. The pooling circuit reduces the quantum’s system size by observing the qubit fraction or applying CNOT Gat...

The Multiscale Entanglement Renormalization Ansatz (MERA) is commonly used to satisfy this structure. MERA is a model for efficiently simulating many-body state quantum systems. MERA now adds qubits to the quantum system, increasing its size exponentially for each depth. 

Unlike the convolution layer, the quantum convolution layer uses a quantum computing environment for filtering.

CNN works primarily by stacking the convolution and pooling layers. The convolution layer uses linear combinations between surrounding pixels to find new hidden data. The pooling layer shrinks the feature map, lowering the learning resources required and preventing overfitting. 

  1. The quantum convolution layer can be constructed as shown in the diagram below. The following is an explanation of how the concept works:
  2. During the encoding process, the pixel data corresponding to the filter size is stored in qubits.
  3. Filters in learnable quantum circuits ca...

Among many classification models, the Convolutional Neural Network (CNN) has demonstrated exceptional performance in computer vision. Photographs and other images that reflect the real world have a high correlation between surrounding pixels. 

Many studies have been published that combine the quantum computing system and the CNN model is capable of solving the problems of the real world which are difficult with machine learning using the Quantum Convolutional Neural Network (QCNN).

Quantum computing is gaining traction as a new way to solve problems that traditional computing techniques can’t solve. Quantum computers have a different computing environment than traditional computers. 

QCNN, or Quantum Convolutional Neural Network extends the key features and structures of existing CNN to quantum systems. When a quantum physics problem defined in the many-body Hilbert space is transferred to a classical computing environment, the data size grows exponentially in proportion to t...

Now, let us have a look at the architecture of the QCNN model.

6. The first step’s encoding is a process that converts classical information into quantum information. The simplest method is to apply a rotation gate to qubits that correspond to pixel data. Of course, different encoding methods exist, and the encoding method chosen can affect the number of qub...

A combination of multiple gates can be used to create the random quantum circuit in the second step. By adding variable gates, the circuit can also perform optimization using the gradient descent method. This circuit can be designed in a variety of ways, each of which has an impact on learning pe...

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