Quantum generative adversarial networks for learning and loading quantum image in noisy environment

2021 ◽  
pp. 2150360
Author(s):  
Wanghao Ren ◽  
Zhiming Li ◽  
Yiming Huang ◽  
Runqiu Guo ◽  
Lansheng Feng ◽  
...  
Keyword(s):  
Image Processing ◽  
Quantum Circuit ◽  
Quantum Image Processing ◽  
Quantum Circuits ◽  
Generative Adversarial Networks ◽  
Channel Noise ◽  
Quantum Image ◽  
Adversarial Networks ◽  
Potential Applications ◽  
Classical Image

Quantum machine learning is expected to be one of the potential applications that can be realized in the near future. Finding potential applications for it has become one of the hot topics in the quantum computing community. With the increase of digital image processing, researchers try to use quantum image processing instead of classical image processing to improve the ability of image processing. Inspired by previous studies on the adversarial quantum circuit learning, we introduce a quantum generative adversarial framework for loading and learning a quantum image. In this paper, we extend quantum generative adversarial networks to the quantum image processing field and show how to learning and loading an classical image using quantum circuits. By reducing quantum gates without gradient changes, we reduced the number of basic quantum building block from 15 to 13. Our framework effectively generates pure state subject to bit flip, bit phase flip, phase flip, and depolarizing channel noise. We numerically simulate the loading and learning of classical images on the MINST database and CIFAR-10 database. In the quantum image processing field, our framework can be used to learn a quantum image as a subroutine of other quantum circuits. Through numerical simulation, our method can still quickly converge under the influence of a variety of noises.

Medical Data Are Safe

2019 ◽  
pp. 142-165
Author(s):  
Padmapriya Praveenkumar ◽  
Santhiyadevi R. ◽  
Amirtharajan R.
Keyword(s):  
Image Processing ◽  
Hash Function ◽  
Medical Data ◽  
Quantum Image Processing ◽  
Second Step ◽  
Encryption Scheme ◽  
Quantum Image ◽  
Image Communication ◽  
Medical Diagnostic ◽  
Classical Image

In this internet era, transferring and preservation of medical diagnostic reports and images across the globe have become inevitable for the collaborative tele-diagnosis and tele-surgery. Consequently, it is of prime importance to protect it from unauthorized users and to confirm integrity and privacy of the user. Quantum image processing (QIP) paves a way by integrating security algorithms in protecting and safeguarding medical images. This chapter proposes a quantum-assisted encryption scheme by making use of quantum gates, chaotic maps, and hash function to provide reversibility, ergodicity, and integrity, respectively. The first step in any quantum-related image communication is the representation of the classical image into quantum. It has been carried out using novel enhanced quantum representation (NEQR) format, where it uses two entangled qubit sequences to hoard the location and its pixel values of an image. The second step is performing transformations like confusion, diffusion, and permutation to provide an uncorrelated encrypted image.

Image Representation, Filtering, and Natural Computing in a Multivalued Quantum System

2016 ◽  
pp. 28-56 ◽  
Author(s):  
Sanjay Chakraborty ◽  
Lopamudra Dey
Keyword(s):  
Image Processing ◽  
Quantum System ◽  
Quantum Physics ◽  
Image Representation ◽  
Quantum Image Processing ◽  
Natural Computing ◽  
Quantum Image ◽  
Image Pixels ◽  
Classical Image ◽  
And Storage

Image processing on quantum platform is a hot topic for researchers now a day. Inspired from the idea of quantum physics, researchers are trying to shift their focus from classical image processing towards quantum image processing. Storing and representation of images in a binary and ternary quantum system is always one of the major issues in quantum image processing. This chapter mainly deals with several issues regarding various types of image representation and storage techniques in a binary as well as ternary quantum system. How image pixels can be organized and retrieved based on their positions and intensity values in 2-states and 3-states quantum systems is explained here in detail. Beside that it also deals with the topic that focuses on the clear filteration of images in quantum system to remove unwanted noises. This chapter also deals with those important applications (like Quantum image compression, Quantum edge detection, Quantum Histogram etc.) where quantum image processing associated with some of the natural computing techniques (like AI, ANN, ACO, etc.).

Medical Data Are Safe

2021 ◽  
pp. 302-324
Author(s):  
Padmapriya Praveenkumar ◽  
Santhiyadevi R. ◽  
Amirtharajan R.
Keyword(s):  
Image Processing ◽  
Hash Function ◽  
Medical Data ◽  
Quantum Image Processing ◽  
Second Step ◽  
Encryption Scheme ◽  
Quantum Image ◽  
Image Communication ◽  
Medical Diagnostic ◽  
Classical Image

In this internet era, transferring and preservation of medical diagnostic reports and images across the globe have become inevitable for the collaborative tele-diagnosis and tele-surgery. Consequently, it is of prime importance to protect it from unauthorized users and to confirm integrity and privacy of the user. Quantum image processing (QIP) paves a way by integrating security algorithms in protecting and safeguarding medical images. This chapter proposes a quantum-assisted encryption scheme by making use of quantum gates, chaotic maps, and hash function to provide reversibility, ergodicity, and integrity, respectively. The first step in any quantum-related image communication is the representation of the classical image into quantum. It has been carried out using novel enhanced quantum representation (NEQR) format, where it uses two entangled qubit sequences to hoard the location and its pixel values of an image. The second step is performing transformations like confusion, diffusion, and permutation to provide an uncorrelated encrypted image.

scholarly journals Quantum Image Processing: Opportunities and Challenges

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Yue Ruan ◽  
Xiling Xue ◽  
Yuanxia Shen
Keyword(s):  
Image Processing ◽  
Quantum Mechanics ◽  
Quantum Computing ◽  
Quantum Computer ◽  
Quantum Image Processing ◽  
Quantum Image ◽  
Intermediate Scale ◽  
Classical Image ◽  
Image Format ◽  
Significant Opportunity

Quantum image processing (QIP) is a research branch of quantum information and quantum computing. It studies how to take advantage of quantum mechanics’ properties to represent images in a quantum computer and then, based on that image format, implement various image operations. Due to the quantum parallel computing derived from quantum state superposition and entanglement, QIP has natural advantages over classical image processing. But some related works misuse the notion of quantum superiority and mislead the research of QIP, which leads to a big controversy. In this paper, after describing this field’s research status, we list and analyze the doubts about QIP and argue “quantum image classification and recognition” would be the most significant opportunity to exhibit the real quantum superiority. We present the reasons for this judgment and dwell on the challenges for this opportunity in the era of NISQ (Noisy Intermediate-Scale Quantum).

Image Representation, Filtering, and Natural Computing in a Multivalued Quantum System

2016 ◽  
pp. 689-717 ◽  
Author(s):  
Sanjay Chakraborty ◽  
Lopamudra Dey
Keyword(s):  
Image Processing ◽  
Quantum System ◽  
Quantum Physics ◽  
Image Representation ◽  
Quantum Image Processing ◽  
Natural Computing ◽  
Quantum Image ◽  
Image Pixels ◽  
Classical Image ◽  
And Storage

Image processing on quantum platform is a hot topic for researchers now a day. Inspired from the idea of quantum physics, researchers are trying to shift their focus from classical image processing towards quantum image processing. Storing and representation of images in a binary and ternary quantum system is always one of the major issues in quantum image processing. This chapter mainly deals with several issues regarding various types of image representation and storage techniques in a binary as well as ternary quantum system. How image pixels can be organized and retrieved based on their positions and intensity values in 2-states and 3-states quantum systems is explained here in detail. Beside that it also deals with the topic that focuses on the clear filteration of images in quantum system to remove unwanted noises. This chapter also deals with those important applications (like Quantum image compression, Quantum edge detection, Quantum Histogram etc.) where quantum image processing associated with some of the natural computing techniques (like AI, ANN, ACO, etc.).

scholarly journals Quantum Dilation and Erosion

2020 ◽  
Vol 10 (11) ◽  
pp. 4040 ◽  
Author(s):  
Shi-Yuan Ma ◽  
Ashraf Khalil ◽  
Hassan Hajjdiab ◽  
Hichem Eleuch
Keyword(s):  
Image Processing ◽  
Quantum Algorithms ◽  
Quantum Circuit ◽  
Object Identification ◽  
Image Features ◽  
Imaging Processing ◽  
Other Information ◽  
Potential Applications ◽  
Classical Image ◽  
Quantum Operators

The dilation and erosion operations are the first fundamental step in classical image processing. They are important in many image processing algorithms to extract basic image features, such as geometric shapes; such shapes are then fed to higher level algorithms for object identification and recognition. In this paper, we present an improved quantum method to realize dilation and erosion in imaging processing. Unlike in the classical way, in the quantum version of imaging processing, all of the information is stored in quantum bits (qubits). We use qubits to code the location and other information of each pixel of the images and apply quantum operators (or quantum gates) to accomplish specific functions. Because of quantum entanglement and other nonintuitive features in quantum mechanics, qubits have many advantages over classical bits, but their nature presents challenges in designing quantum algorithms. We first built the quantum circuit theoretically, and then ran it on the IBM Quantum Experience platform to test and process real images. With this algorithm, we are looking forward to more potential applications in quantum computation.

scholarly journals Quantum Generative Adversarial Networks for learning and loading random distributions

2019 ◽  
Vol 5 (1) ◽  
Author(s):  
Christa Zoufal ◽  
Aurélien Lucchi ◽  
Stefan Woerner
Keyword(s):  
Quantum State ◽  
Quantum Channel ◽  
Quantum Algorithms ◽  
Probability Distributions ◽  
Quantum Algorithm ◽  
Quantum Circuit ◽  
Quantum Simulation ◽  
Quantum States ◽  
Generative Adversarial Networks ◽  
Adversarial Networks

AbstractQuantum algorithms have the potential to outperform their classical counterparts in a variety of tasks. The realization of the advantage often requires the ability to load classical data efficiently into quantum states. However, the best known methods require $${\mathcal{O}}\left({2}^{n}\right)$$O2n gates to load an exact representation of a generic data structure into an $$n$$n-qubit state. This scaling can easily predominate the complexity of a quantum algorithm and, thereby, impair potential quantum advantage. Our work presents a hybrid quantum-classical algorithm for efficient, approximate quantum state loading. More precisely, we use quantum Generative Adversarial Networks (qGANs) to facilitate efficient learning and loading of generic probability distributions - implicitly given by data samples - into quantum states. Through the interplay of a quantum channel, such as a variational quantum circuit, and a classical neural network, the qGAN can learn a representation of the probability distribution underlying the data samples and load it into a quantum state. The loading requires $${\mathcal{O}}\left(poly\left(n\right)\right)$$Opolyn gates and can thus enable the use of potentially advantageous quantum algorithms, such as Quantum Amplitude Estimation. We implement the qGAN distribution learning and loading method with Qiskit and test it using a quantum simulation as well as actual quantum processors provided by the IBM Q Experience. Furthermore, we employ quantum simulation to demonstrate the use of the trained quantum channel in a quantum finance application.

A Survey on Quantum Image Processing

2018 ◽  
Vol 27 (4) ◽  
pp. 718-727 ◽  
Author(s):  
Yongquan Cai ◽  
Xiaowei Lu ◽  
Nan Jiang
Keyword(s):  
Image Processing ◽  
Quantum Image Processing ◽  
Quantum Image
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