A study and analysis of a discrete quantum walk-based hybrid clustering approach using d-regular bipartite graph and 1D lattice
03 Apr 2019-International Journal of Quantum Information (World Scientific Publishing Company)-Vol. 17, Iss: 02, pp 1950016
TL;DR: Comparisons are demonstrated of the proposed hybrid quantum clustering algorithm with some state-of-the-art clustering algorithms in terms of clustering accuracy and time complexity analysis and the proposed QWBHC algorithm achieves good performance.
Abstract: Traditional machine learning shares several benefits with quantum information processing field. The study of machine learning with quantum mechanics is called quantum machine learning. Data cluster...
TL;DR: The result analysis shows that the proposed quantum image denoising technique has better visual quality in terms of PSNR,MSE and QIFM values Compare to others.
Abstract: The amalgamation of ‘Quantum computing’ with image processing represents the various ways of handling images for different purposes. In this paper,an image denoising scheme based on quantum wavelet transform is proposed.A noisy image is embedded into the wavelet coefficients of the original image. As a result,it affects the visual quality of the original image. The quantum Daubechis kernel of 4th order is used to extract wavelet coefficients from the resultant image. Then a quantum oracle is implemented with a suitable thresholding function to decompose the wavelet coefficients into a greater effect applicable for the original image and lower effect for the noisy image wavelet coefficients. However,original image wavelet coefficients are greater than the noisy wavelet coefficients.A detail computational time complexity analysis is given and compared with some state-of-art denoising techniques. The result analysis shows that the proposed quantum image denoising technique has better visual quality in terms of PSNR,MSE and QIFM values Compare to others.
TL;DR: In this article , a comparative study of commonly applied consensus algorithms for distributed averaging in d-regular bipartite graphs is presented, where the authors examine the performance of these algorithms with bounded execution in this topology in order to identify which algorithm can achieve the consensus despite no reconfiguration and find the best-performing algorithm in these graphs.
Abstract: Consensus-based data aggregation in d-regular bipartite graphs poses a challenging task for the scientific community since some of these algorithms diverge in this critical graph topology. Nevertheless, one can see a lack of scientific studies dealing with this topic in the literature. Motivated by our recent research concerned with this issue, we provide a comparative study of frequently applied consensus algorithms for distributed averaging in d-regular bipartite graphs in this paper. More specifically, we examine the performance of these algorithms with bounded execution in this topology in order to identify which algorithm can achieve the consensus despite no reconfiguration and find the best-performing algorithm in these graphs. In the experimental part, we apply the number of iterations required for consensus to evaluate the performance of the algorithms in randomly generated regular bipartite graphs with various connectivities and for three configurations of the applied stopping criterion, allowing us to identify the optimal distributed consensus algorithm for this graph topology. Moreover, the obtained experimental results presented in this paper are compared to other scientific manuscripts where the analyzed algorithms are examined in non-regular non-bipartite topologies.
TL;DR: In this article , a quantum walks-based classification model is proposed for intrusion detection in cloud computing, which concentrates feature information of data via principal component analysis, and then aggregates the concentrated data in the way of quantum walks by a training-free clustering algorithm.
Abstract: Cloud computing is considerably investigable and adoptable in both industry and academia, and Software Defined Networking (SDN) has been applied in cloud computing. Although SDN mitigates some security issues in cloud computing, new security issues related to its own architecture are also introduced. In this paper, we propose a quantum walks-based classification model which is available for intrusion detection in cloud computing. The proposed model concentrates feature information of data via Principal Component Analysis, and then aggregates the concentrated data in the way of quantum walks by a training-free clustering algorithm. The clustering algorithm constructs coin transformation and conditional shift transformation based on transition probabilities to move similar data toward each other. To enhance the usability of the proposed model in cloud computing security, we propose a new cloud architecture which adds security layer in SDN to ponder the protection of cloud computing fundamentally, and simplify transition probabilities equations of clustering algorithm without affecting clustering accuracy, decreasing the time complexity from O(nk2) to O(nk). The experimental results on popular datasets (Accuracy: 99.4% on InSDN, 95.8% on NSL-KDD, 98% on UNSW-NB15 and 96.4% on CSE-CIC-IDS2018) revealed that the proposed model is effective dealing with attacks on SDN-based cloud computing, and is able to maintain stable and excellent attack identification ability under different traffic intensities.
TL;DR: A number of physical systems, spanning much of modern physics, are being developed for this task, ranging from single particles of light to superconducting circuits, and it is not yet clear which, if any, will ultimately prove successful as discussed by the authors.
Abstract: Quantum mechanics---the theory describing the fundamental workings of nature---is famously counterintuitive: it predicts that a particle can be in two places at the same time, and that two remote particles can be inextricably and instantaneously linked These predictions have been the topic of intense metaphysical debate ever since the theory's inception early last century However, supreme predictive power combined with direct experimental observation of some of these unusual phenomena leave little doubt as to its fundamental correctness In fact, without quantum mechanics we could not explain the workings of a laser, nor indeed how a fridge magnet operates Over the last several decades quantum information science has emerged to seek answers to the question: can we gain some advantage by storing, transmitting and processing information encoded in systems that exhibit these unique quantum properties? Today it is understood that the answer is yes Many research groups around the world are working towards one of the most ambitious goals humankind has ever embarked upon: a quantum computer that promises to exponentially improve computational power for particular tasks A number of physical systems, spanning much of modern physics, are being developed for this task---ranging from single particles of light to superconducting circuits---and it is not yet clear which, if any, will ultimately prove successful Here we describe the latest developments for each of the leading approaches and explain what the major challenges are for the future
TL;DR: This article will introduce quantum random walks, review some of their properties and outline their striking differences to classical walks, introducing some of the main concepts and language of present day quantum information science in this context.
Abstract: This article aims to provide an introductory survey on quantum random walks. Starting from a physical effect to illustrate the main ideas we will introduce quantum random walks, review some of their properties and outline their striking differences to classical walks. We will touch upon both physical effects and computer science applications, introducing some of the main concepts and language of present day quantum information science in this context. We will mention recent developments in this new area and outline some open questions.
TL;DR: This paper has reviewed several algorithms based on both discrete- and continuous-time quantum walks as well as a most important result: the computational universality of both continuous- and discrete- time quantum walks.
Abstract: Quantum walks, the quantum mechanical counterpart of classical random walks, is an advanced tool for building quantum algorithms that has been recently shown to constitute a universal model of quantum computation. Quantum walks is now a solid field of research of quantum computation full of exciting open problems for physicists, computer scientists and engineers. In this paper we review theoretical advances on the foundations of both discrete- and continuous-time quantum walks, together with the role that randomness plays in quantum walks, the connections between the mathematical models of coined discrete quantum walks and continuous quantum walks, the quantumness of quantum walks, a summary of papers published on discrete quantum walks and entanglement as well as a succinct review of experimental proposals and realizations of discrete-time quantum walks. Furthermore, we have reviewed several algorithms based on both discrete- and continuous-time quantum walks as well as a most important result: the computational universality of both continuous- and discrete-time quantum walks.
TL;DR: A systematic overview of the emerging field of quantum machine learning can be found in this paper, which presents the approaches as well as technical details in an accessible way, and discusses the potential of a future theory of quantum learning.
Abstract: Machine learning algorithms learn a desired input-output relation from examples in order to interpret new inputs. This is important for tasks such as image and speech recognition or strategy optimisation, with growing applications in the IT industry. In the last couple of years, researchers investigated if quantum computing can help to improve classical machine learning algorithms. Ideas range from running computationally costly algorithms or their subroutines efficiently on a quantum computer to the translation of stochastic methods into the language of quantum theory. This contribution gives a systematic overview of the emerging field of quantum machine learning. It presents the approaches as well as technical details in an accessible way, and discusses the potential of a future theory of quantum learning.
TL;DR: A brief overview of quantum walks, with emphasis on their algorithmic applications, can be found in this article, where the authors describe quantum walks as quantum counterparts of Markov chains, and present several applications of quantum walk.
Abstract: Quantum walks are quantum counterparts of Markov chains. In this article, we give a brief overview of quantum walks, with emphasis on their algorithmic applications.
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