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

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.

Development and Research of Quantum Transformation Models

The article discusses the theory of quantum image processing and quantum image manipulation (fast geometric transformations, including the flip, orthogonal rotations and limited geometric transformations). In this work we have investigated the area of quantum image processing and have tested the FPQI model. Models (developed in the work and others) were investigated by explicitly modeling quantum behavior on a large quantum system. A basic scheme for encoding 2×2 image has been created to gain a clearer understanding of the mechanics of quantum image models.

Quantum Image Processing: Opportunities and Challenges

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).

Medical Data Are Safe

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.