Keep reading for FREE
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 use of quantum computation and CNN to develop a more efficient and outperforming technique that can be applied to solve complex machine learning tasks.This technique which integrates both CNN and quantum computing is referred o as Quantum Convolutional Neural Network(QCNN).In this post, we will have an in-depth understanding of QCNN with its paradigm and applications.The following r the key points to be discussed in this article.
4
62 reads
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.
The fully-connected layer, which is a fundamental model in deep learning, performed well in machine learning, but there is no way to maintain the correlation. CNN, on the other hand, can directly store correlation information, resulting in a more accurate performance evaluation.
4
24 reads
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.
The classification result is obtained using the fully connected layer after the data size has been reduced sufficiently by repeatedly applying these layers. For better results, the loss between the acquired label and the actual label can be used to train the model using a gradient descent method or other optimizers.
4
16 reads
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).
There is a method for solving quantum physics problems efficiently by applying the CNN structure to a quantum system, as well as a method for improving performance by adding a quantum system to problems previously solved by CNN.
Before proceeding to the QCNN, we first need to understand what is Quantum computing and computation.
4
13 reads
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.
Quantum computers, in particular, can use superposition and entanglement, which are not seen in traditional computing environments, to achieve high performance through qubit parallelism. Here qubit is referred to as the quantum bit which is basically a unit of quantum information.
4
14 reads
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.
4
13 reads
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 the system size, making it unsuitable for efficient solutions. Because data in a quantum environment can be expressed using qubits, the problem can be avoided by applying a CNN structure to a quantum computer.
4
10 reads
Now, let us have a look at the architecture of the QCNN model.
As shown in the above architecture, the QCNN model applies the convolution layer and the pooling layer which are the key features of CNN, to the quantum systems.
4
9 reads
4
5 reads
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.
This MERA is used in the opposite direction by QCNN. The reversed MERA, which is suitable as a model of QCNN, reduces the size of the quantum system exponentially from the given data.
4
5 reads
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 section, we’ll look at how the QCNN can help with image classification.
The quantum convolution layer is a layer in a quantum system that behaves like a convolution layer. To obtain feature maps composed of new data, the quantum convolution layer applies a filter to the input feature map.
4
5 reads
Unlike the convolution layer, the quantum convolution layer uses a quantum computing environment for filtering.
Quantum computers offer superposition and parallel computation, which are not available in classical computing and can reduce learning and evaluation time. Existing quantum computers, on the other hand, are still limited to small quantum systems.
Small quantum computers can construct the quantum convolution layer because it does not apply the entire image map to a quantum system at once but rather processes it as much as the filter size at a time.
4
3 reads
4
6 reads
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 qubits required as well as the learning efficiency. The third decoding process is based on the measurement of one or more quantum states. Classical data is determined by measuring quantum states.
4
3 reads
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 performance.
4
3 reads
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 from this discussion, if you want to look at a practical implementation of the QCNN, I recommend that you look at the TensorFlow implementation and the researcher’s team as mentioned in the introduction.
4
2 reads
CURATED BY
卐 || एकं सत विप्रा बहुधा वदन्ति || Enthusiast || Collection Of Some Best Reads || Decentralizing...
MORE LIKE THIS
Ready for the next level?
Read Like a Pro
Explore the World’s
Best Ideas
Save ideas for later reading, for personalized stashes, or for remembering it later.
Start
31 IDEAS
Start
44 IDEAS
# Personal Growth
Take Your Ideas
Anywhere
Just press play and we take care of the words.
No Internet access? No problem. Within the mobile app, all your ideas are available, even when offline.
Ideas for your next work project? Quotes that inspire you? Put them in the right place so you never lose them.
Start
47 IDEAS
Start
75 IDEAS
My Stashes
Join
2 Million Stashers
4.8
5,740 Reviews
App Store
4.7
72,690 Reviews
Google Play
Shankul Varada
Best app ever! You heard it right. This app has helped me get back on my quest to get things done while equipping myself with knowledge everyday.
“
samz905
Don’t look further if you love learning new things. A refreshing concept that provides quick ideas for busy thought leaders.
“
Ashley Anthony
This app is LOADED with RELEVANT, HELPFUL, AND EDUCATIONAL material. It is creatively intellectual, yet minimal enough to not overstimulate and create a learning block. I am exceptionally impressed with this app!
“
Sean Green
Great interesting short snippets of informative articles. Highly recommended to anyone who loves information and lacks patience.
“
Ghazala Begum
Even five minutes a day will improve your thinking. I've come across new ideas and learnt to improve existing ways to become more motivated, confident and happier.
“
Giovanna Scalzone
Brilliant. It feels fresh and encouraging. So many interesting pieces of information that are just enough to absorb and apply. So happy I found this.
“
Laetitia Berton
I have only been using it for a few days now, but I have found answers to questions I had never consciously formulated, or to problems I face everyday at work or at home. I wish I had found this earlier, highly recommended!
“
Jamyson Haug
Great for quick bits of information and interesting ideas around whatever topics you are interested in. Visually, it looks great as well.
“
Read & Learn
20x Faster
without
deepstash
with
deepstash
with
deepstash
Access to 200,000+ ideas
—
Access to the mobile app
—
Unlimited idea saving & library
—
—
Unlimited history
—
—
Unlimited listening to ideas
—
—
Downloading & offline access
—
—
Personalized recommendations
—
—
FAQ
Claim Your Limited Offer
Get Deepstash Pro
Supercharge your mind with one idea per day
Enter your email and spend 1 minute every day to learn something new.
I agree to receive email updates