Quantum image processing algorithm using edge extraction based on Kirsch operator

Xu, Pengfei; He, Zhenxing; Qiu, Tianhui; Ma, Hongyang

doi:10.1364/oe.386283

Cited by 43 publications

(18 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To summarize, the wiring methods go against everything recommended in superconducting and optical circuits, for this reason, the positive statements made in the Quantum Image Processing (QImP) literature [31][32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47] about these methods are incomprehensible. At the other extreme, we have the three best QImP papers ever written, which among their main merits we can highlight the excellent experimental frameworks carried out in their respective laboratories by the authors [11,48,49], demonstrating that QImP can be done with quality and scientific rigor.…”

Section: Setupmentioning

confidence: 99%

Analysis of Five Techniques for the Internal Representation of a Digital Image Inside a Quantum Processor

2021

View full text Add to dashboard Cite

In this paper, five techniques of representing a digital image inside a quantum processor are compared. The techniques are: flexible representation of quantum images (FRQI), novel enhanced quantum representation (NEQR), generalized quantum image representation (GQIR), multi-channel representation for quantum images (MCQI), and quantum Boolean image processing (QBIP). The comparison will be based on implementations on the Quirk simulator, and on the IBM Q Experience processors, from the point of view of performance, robustness (noise immunity), deterioration of the outcomes due to decoherence, and technical viability.

show abstract

Section: Setupmentioning

confidence: 99%

Analysis of Five Techniques for the Internal Representation of a Digital Image Inside a Quantum Processor

2021

View full text Add to dashboard Cite

show abstract

“…Quantum mechanics is a set of rules constructed by mathematical framework or physical theory. As a new computational framework for integrating the concepts of quantum mechanics, quantum computing [1,2] utilizes the properties of quantum mechanics to process both quantum and classical information [3,4], which may cause an insurmountable gap between quantum and classical computers. As quantum technology is developing, noisy intermediate-scale quantum computers (NISQ) have begun to deal with some relatively complex computing tasks, and their computing power has surpassed classical computers in some tasks [5].…”

Section: Introductionmentioning

confidence: 99%

An Image Classification Algorithm Based on Hybrid Quantum Classical Convolutional Neural Network

Chu

Liu

et al. 2022

Quantum Engineering

View full text Add to dashboard Cite

Quantum machine learning is emerging as a strategy to solve real-world problems. As a quantum computing model, parameterized quantum circuits provide an approach for constructing quantum machine learning algorithms, which may either realize computational acceleration or achieve better algorithm performance than classical algorithms. Based on the parameterized quantum circuit, we propose a hybrid quantum-classical convolutional neural network (HQCCNN) model for image classification that comprises both quantum and classical components. The quantum convolutional layer is designed using a parameterized quantum circuit. It is used to perform linear unitary transformation on the quantum state to extract hidden information. In addition, the quantum pooling unit is used to perform pooling operations. After the evolution of the quantum system, we measure the quantum state and input the measurement results into a classical fully connected layer for further processing. We demonstrate its potential by applying HQCCNN to the MNIST dataset. Compared to a convolutional neural network in a similar architecture, the results reveal that HQCCNN has a faster training speed and higher testing set accuracy than a convolutional neural network.

show abstract

“…With the continuous development of quantum computing and quantum communication, many quantum algorithms and quantum technologies have emerged [25][26][27][28][29][30][31][32][33] . Quantum random walk is generated by applying classical random walk to quantum computing, and it plays an important part in a number of quantum algorithms [34][35][36][37] .…”

Section: Introductionmentioning

confidence: 99%

Color Image Encryption Scheme Based On Alternate Quantum Walk and Controlled Rubik’s Cube

Zhao

Zhang

Jiang

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

Aiming at solving the trouble that digital image information is easily intercepted and tampered during transmission, we proposed a color image encryption scheme based on alternate quantum random walk and controlled Rubik’s Cube transformation. At the first, the color image is separated into three channels: channel R, channel G and channel B. Besides, a random sequence is generated by alternate quantum walk. Then the six faces of the Rubik’s Cube are decomposed and arranged in a specific order on a two-dimensional plane, and each pixel of the image is randomly mapped to the Rubik’s Cube. The whirling of the Rubik’s Cube is controlled by a random sequence to realize image scrambling and encryption. The scrambled image acquired by Rubik’s Cube whirling and the random sequence received by alternate quantum walk are bitwise-XORed to obtain a single-channel encrypted image. Finally the three-channel image is merged to acquire the final encrypted image. The decryption procedure is the reverse procedure of the encryption procedure. The key space of this scheme is theoretically infinite. After simulation experiments, the information entropy after encryption reaches 7.999, the NPCR is 99.5978%, and the UACI is 33.4317%. The encryption scheme with high robustness and security has a excellent encryption effect which is effective to resist statistical attacks, force attacks, and other differential attacks.

show abstract

Quantum image processing algorithm using edge extraction based on Kirsch operator

Cited by 43 publications

References 27 publications

Analysis of Five Techniques for the Internal Representation of a Digital Image Inside a Quantum Processor

Analysis of Five Techniques for the Internal Representation of a Digital Image Inside a Quantum Processor

An Image Classification Algorithm Based on Hybrid Quantum Classical Convolutional Neural Network

Color Image Encryption Scheme Based On Alternate Quantum Walk and Controlled Rubik’s Cube

Contact Info

Product

Resources

About