Comparing Concepts of Quantum and Classical Neural Network Models for Image Classification Task

Potempa, R.; Porębski, Sebastian

doi:10.1007/978-3-030-81523-3_6

Cited by 4 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Further, this work presents fundamental about quantum properties such as superposition, entanglement, and quantum programming tools such as Qiskit (IBM), pyQuil (Google), etc. In Potempa and Porebski ( 2022 ) the authors Comparing Concepts of Quantum and Classical Neural Network Models for Image Classification Task. The comparative results of two models: classical and quantum neural networks of a similar number of training parameters, indicate that the quantum network, although its simulation is time-consuming, overcomes the classical network it has better convergence and achieves higher training and testing accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

A review on quantum computing and deep learning algorithms and their applications

Valdez

Melín

2022

Soft Comput

View full text Add to dashboard Cite

In this paper, we describe a review concerning the Quantum Computing (QC) and Deep Learning (DL) areas and their applications in Computational Intelligence (CI). Quantum algorithms (QAs), engage the rules of quantum mechanics to solve problems using quantum information, where the quantum information is concerning the state of a quantum system, which can be manipulated using quantum information algorithms and other processing techniques. Nowadays, many QAs have been proposed, whose general conclusion is that using the effects of quantum mechanics results in a significant speedup (exponential, polynomial, super polynomial) over the traditional algorithms. This implies that some complex problems currently intractable with traditional algorithms can be solved with QA. On the other hand, DL algorithms offer what is known as machine learning techniques. DL is concerned with teaching a computer to filter inputs through layers to learn how to predict and classify information. Observations can be in the form of plain text, images, or sound. The inspiration for deep learning is the way that the human brain filters information. Therefore, in this research, we analyzed these two areas to observe the most relevant works and applications developed by the researchers in the world.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

A review on quantum computing and deep learning algorithms and their applications

Valdez

Melín

2022

Soft Comput

View full text Add to dashboard Cite

show abstract

“…The algorithm applied allowed us to obtain much better classification accuracy than it was in the case of [18], whereby in our experiment, classification was performed using 43 classes. The results obtained are at a level similar to some of the experiments carried out in [12].…”

Section: Qnn For Traffic Sign Recognitionmentioning

confidence: 99%

“…They also show better generalization capabilities even with small amounts of training data and require several times fewer epochs compared to classical networks. QNNs were successfully used, among others, in the following applications: tree recognition in aerial space of California [3], cancer recognition [10], facial expression recognition [17], vehicle classification [19], traffic sign recognition from the LISA database considering the vulnerability of adversarial attacks [12], handwriting recognition [22], [15], [18], [21], [7], [8],…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Traffic Sign Classification Using Deep and Quantum Neural Networks

Kuros¹,

Kryjak²

2022

Preprint

View full text Add to dashboard Cite

<p>Quantum Neural Networks (QNNs) are an emerging technology that can be used in many applications including computer vision. In this paper, we presented a traffic sign classification system implemented using a hybrid quantum-classical convolutional neural network. Experiments on the German Traffic Sign Recognition Benchmark dataset indicate that currently QNN do not outperform classical DCNN (Deep Convolutuional Neural Networks), yet still provide an accuracy of over 90% and are a definitely promising solution for advanced computer vision.</p>

show abstract

“…Zhou et al., Suen (1999) adopted a quantum neural network (QNN) with fuzzy features to detect handwritten numerals. Several research works (Potempa & Porebski, 2022; Riaz et al., 2023; Trochun et al., 2021) proposed QCNN algorithms to classify images of MNIST datasets (Yann et al., 1999) and MNIST Fashion datasets (Henderson et al., 2020; Xiao et al., 2017). Likewise, the adoption of QCNNs techniques in classifying images of the CIFAR‐10 dataset (Krizhevsky & Hinton, 2009) has been reported in the literature (Riaz et al., 2023; Raj & Vaithiyashankar, 2022).…”

Section: Introductionmentioning

confidence: 99%

Multiclass seismic damage detection of buildings using quantum convolutional neural network

Bhatta,

Dang

2023

Computer aided Civil Eng

View full text Add to dashboard Cite

The traditional visual inspection technique for damage assessment of buildings immediately after an earthquake can be time‐consuming, labor‐intensive, and risky. Numerous studies have been carried out using deep learning techniques, particularly convolutional neural network (CNN), to evaluate the damage to building structures after an earthquake using buildings’ damage images. Quantum computing, on the other hand, is a computing environment that can exploit superposition and entanglement, which are not available in classical computing environments, to achieve higher performance using parallelism between qubits. This paper presents a novel quantum CNN (QCNN) approach to detect damage to reinforced concrete (RC) buildings from images after the earthquake. The QCNN model is developed and trained using the RC building damaged images collected from past earthquakes. The performance of this model is evaluated based on the multiclass damage detection ability of the real‐world RC building damaged images collected from the recent earthquake in Turkey in February 2023. Furthermore, the seismic damage detection accuracy obtained from the QCNN model is compared with various CNN architecture results.

show abstract

Comparing Concepts of Quantum and Classical Neural Network Models for Image Classification Task

Cited by 4 publications

References 14 publications

A review on quantum computing and deep learning algorithms and their applications

A review on quantum computing and deep learning algorithms and their applications

Traffic Sign Classification Using Deep and Quantum Neural Networks

Multiclass seismic damage detection of buildings using quantum convolutional neural network

Contact Info

Product

Resources

About