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7th International Conference on Signal processing and information Security, ICSPIS 2024

作者： Siddiqui, Lubna Mani, Ashish Singh, Jaspal Amity University Amity School of Engineering and Technology Noida India Amity University Amity Innovation and Design Centre Noida India Centre for Development of Advanced Computing Chandigarh India

ISBN: (纸本)9798350368673

This document aims to reaffirm the superiority of quantum-inspired evolutionary algorithms(QiEA) over conventional algorithms. The study proposes a novel QiEA with sorted mutation and greedy local restart (SMGLR) algorithm for finding the optimal coefficients of a filter using a multi-objective optimization function. PCG signals sourced from the Washington Hearts Sounds and Murmurs database and the Michigan Heart Sounds database were filtered using this enhanced filter. Comparative analysis was conducted against the proposed algorithm and established techniques such as Wavelet-based denoising and Variable Mode decomposition. Results: The results revealed that the filter designed using QiEA-SMGLR exhibited superior performance in terms of improved Signal-to-Noise Ratio (SNR) in both comparisons. A comprehensive statistical analysis yielded a correlation coefficient of close to 0.99 indicating strong signal integrity. The QiEA-SMGLR algorithm outperforms Wavelet-based and VMD methods by improving the output SNR by 20% to 100%. This variance can be attributed to the intensity of noise introduced. The result reaffirms the hypothesis that the QiEA-SMGLR algorithm is superior in improving signal clarity and precision over traditionally popular techniques. The improvement in the analysis of Phonocardiogram (PCG) signals could help improve patient diagnostics, amidst the fight against global cardiac problems. © 2024 IEEE.

关键词： quantum computers

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High-Dimensional Data processing Using quantum-Inspired Evolutionary algorithms for Homeland Security Imaging Systems

High-Dimensional Data Processing Using Quantum-Inspired Evol...

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2024 International Conference on Communication, Computer Sciences and Engineering, IC3SE 2024

作者： Alekhya, V. Reddy, N. V. Uma Singh, Jay Boddu, Bhasker Sobti, Rajeev Hameed, Ali Abduhussien Institute Of Aeronautical Engineering Dundigal Hyderabad India New Horizon College Of Engineering Department Of Artificial Intelligence And Machine Learning Bangalore India Gl Bajaj Institute Of Technology & Management U.P. Greater Noida India Mlr Institute Of Technology Department Of Information Technology Telangana Hyderabad India Lovely Professional University Phagwara India College Of Medical Technology The Islamic University Radiology Techniques Department Najaf Iraq

ISBN: (纸本)9798350366846

This research study explores the emerging area of quantum-inspired evolutionary algorithms (QIEAs) applied to high-dimensional data processing, with a focus on homeland security imaging systems. This work attempts to close the paradigm gap in image processing methodologies caused by the growing complexity of security threats. By combining the adaptive processes of evolutionary algorithms with the probabilistic reasoning inherent in quantum computing, the proposed technique creates a potent tool for managing the inherent difficulties of high-dimensional data. The main contribution of this study is the creation of a brand-new QIEA framework that is specifically designed for image analysis in security contexts and exhibits higher accuracy and efficiency than traditional techniques. The quantum bit representation and quantum gate operations, which have been cleverly tailored to the evolutionary algorithm structure, provide the methodological basis, and improve the search capacity in multidimensional data fields. An unparalleled degree of clarity and detail in security photography is made possible by this fusion, which is essential for threat identification and prevention. Experiments conducted on a variety of difficult datasets show that the suggested method is reliable for identifying important characteristics in complicated photos, which is a crucial component of homeland security applications. Beyond only addressing short-term security issues, this study establishes a standard for next investigations into quantum-inspired computing for image processing. © 2024 IEEE.

关键词： Imaging systems

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quantum Enhanced Prediction for Breast Cancer Electronic Health Records

Quantum Enhanced Prediction for Breast Cancer Electronic Hea...

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2024 IEEE International Conference on Signal processing, Informatics, Communication and Energy Systems, SPICES 2024

作者： Rajesh, G. Madhumitha, R. Arunagiri, S.S. Vishal, M. Anna University Department of Information Technology Chennai India

ISBN: (数字)9798350376135

ISBN: (纸本)9798350376135

quantum computing technology allows for more efficient problem-solving and provides essentially distinct answers to computational challenges when compared to classical computations. Machine learning algorithms have come to the fore in medical research and offer promising avenues for increasing the accuracy of cancer diagnosis. Breast cancer represents the majority of cancer cases in women worldwide, making it a serious and widespread public health issue. Improving patient outcomes requires early identification of cancer. The proposed quantum Enhanced Prediction (QEP) project delves into the predictive power of two distinct learning paradigms: Support Vector Machine and quantum Support Vector Machine, using Wisconsin breast cancer dataset. The research underscores the complexity of algorithms in cancer prediction. A departure from the ZZ feature map in QSVM marked the beginning of an intentional shift from traditional design. However, the study looked at the integration of the Z element map and Pauli matrices, providing the foundation for improved accuracy and a better grasp of quantum mechanics in cancer prediction. This conceptual shift intends to investigate the possible influence of quantum computing on breast cancer prediction with more granularity. © 2024 IEEE.

关键词： Electronic health record

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BitBIRCH Clustering Refinement Strategies

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JOURNAL OF CHEMICAL information AND MODELING 2025年第11期65卷 5280-5288页

作者： Perez, Kenneth Lopez Huddleston, Kate Jung, Vicky Miranda-Quintana, Ramon Alain Univ Florida Dept Chem & Quantum Theory Project Gainesville FL 32603 USA

Chemical libraries are becoming not only increasingly bigger, but they are doing so at an accelerated pace. Keeping up with this explosion in chemical data demands more than just hardware upgrades;we need dramatically more efficient algorithms as well. We have been working in this direction, with the introduction of the instant similarity (iSIM) framework, which uses n-ary similarity to speed up the processing of very large sets. Recently, we showed how to use this technique to cluster billions of molecules with unprecedented efficiency through the BitBIRCH algorithm. In this Application Note, we present a package fully dedicated to expanding on the BitBIRCH method, including multiple options that give the user appreciable control over the tree structure, while dramatically improving the quality of the final partitions. Remarkably, this is achieved without compromising the efficiency of the original method. We also present new postprocessing tools that help dissect the clustering information, as well as ample examples showcasing the new functionalities. BitBIRCH is publicly available at: https://***/mqcomplab/bitbirch.

关键词： Clustering algorithms

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Preliminary Study on Reducing Memory Overhead in Accelerating quantum Computer Simulations Using PIM Technology 20

Preliminary Study on Reducing Memory Overhead in Acceleratin...

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20th International SoC Design Conference (ISOCC)

作者： Hour, Leanghok Go, Myeongseong Han, Youngsun Pukyong Natl Univ Dept Artificial Intelligence Convergence Busan South Korea

ISBN: (纸本)9798350327038

Recent developments in quantum algorithms have demonstrated the promise of quantum computers, even in the era of noisy intermediate-scale quantum computing (NISQ). However, accessing real quantum computers remains challenging due to cost and long wait times. Additionally, the limited scale and noise of these computers can impact the outcomes of algorithms. To overcome these challenges, researchers often rely on quantum computer simulators. However, these simulators, which use CPUs and/or GPUs, suffer from high memory overhead. To address this issue, we propose a processing-in-memory (PIM)-based technology for quantum computer simulation. We confirmed that our proposed approach reduces data movement cycles in memory by 2.76 times on average compared to traditional execution on CPUs.

关键词： quantum computer simulation processing-inmemory

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Parametrized quantum Circuits for Reinforcement Learning 4

Parametrized Quantum Circuits for Reinforcement Learning

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4th International Multidisciplinary information Technology and Engineering Conference, IMITEC 2024

作者： Mpofu, K.T. Mthunzi-Kufa, P. Biophotonics Council for Scientific and Industrial Research National Laser Centre Gauteng Pretoria0001 South Africa University of Cape Town Division of Biomedical Engineering Department of Human Biology Western Cape Cape Town7701 South Africa College of Graduate Studies University of South Africa Pretoria South Africa

ISBN: (纸本)9798350387988

Research on quantum computing is still in its infancy, but it has a lot of potential uses. One topic with potential is machine learning, namely in the field of reinforcement learning. This work examines the integration of parametrized quantum circuits (PQC) into reinforcement learning (RL) algorithms, assessing the potential of quantum-enhanced models to solve classical RL tasks. It closely follows the example found on the TensorFlow website. This paper reviews applications of quantum reinforcement learning (QRL). We examine PQCs in a standard RL scenario, the CartPole-v1 environment from Gym, using TensorFlow quantum and Cirq, to evaluate the relative performance of quantum versus conventional models. In comparison to conventional deep neural network (ONN) models, PQCs show slower convergence and higher processing needs, even if they are still able to learn the task and perform competitively. After they are fully trained, the quantum models show unique difficulties during the early training stages and reach a performance stability level like classical methods. This study sheds light on the present constraints as well as possible uses of quantum computing in reinforcement learning, particularly in situations with intricate, high-dimensional settings that prove difficult for classical computers to handle effectively. As we look to the future, we suggest that investigating hybrid quantum-classical algorithms, developing quantum hardware, and using quantum RL for increasingly difficult tasks are essential first steps. The study presents findings from both a classical reinforcement learning algorithm and a quantum integrated reinforcement learning algorithm. To provide a reliable comparison between quantum reinforcement algorithms and their classical equivalents, further work remains. This work lays the groundwork for future advances in the field by investigating the viability and use of quantum algorithms in reinforcement learning, even if it is not particularly unique

关键词： quantum computers

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quantum K-nearest neighbor classification algorithm based on Hamming distance

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quantum information processing 2022年第1期21卷 1-17页

作者： Li, Jing Lin, Song Yu, Kai Guo, Gongde Fujian Normal Univ Coll Math & Informat Fuzhou 350117 Peoples R China Fujian Normal Univ Digital Fujian Internet Things Lab Environm Monit Fuzhou 350117 Peoples R China

K-nearest neighbor classification algorithm is one of the most basic algorithms in machine learning, which determines the sample's category by the similarity between samples. In this paper, we propose a quantum K-nearest neighbor classification algorithm with the Hamming distance. In this algorithm, quantum computation is utilized to obtain the Hamming distance in parallel at first. Then, a core sub-algorithm for searching the minimum of unordered integer sequence is presented to find out the minimum distance. Based on these two sub-algorithms, the whole quantum frame of K-nearest neighbor classification algorithm is presented. At last, it is shown that the proposed algorithm can achieve a significant speedup by analyzing its time complexity briefly.

关键词： quantum machine learning K-nearest neighbor classification quantum algorithm

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Photonic scheme of quantum phase estimation for quantum algorithms via quantum dots

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quantum information processing 2022年第1期21卷 1-21页

作者： Heo, Jino Choi, Seong-Gon Chungbuk Natl Univ Res Inst Comp & Informat Commun Ric Chungdae Ro 1 Cheongju South Korea Chungbuk Natl Univ Coll Elect & Comp Engn Chungdae Ro 1 Cheongju South Korea

Various quantum algorithms depend on quantum phase estimation (QPE) as basic blocks or main subroutines to leverage superposition and entanglement during quantum computations. The QPE algorithm estimates the unknown phase of an eigenvalue corresponding to an eigenstate of an arbitrary unitary operator. We propose the photonic scheme of a QPE scheme comprising controlled-unitary gates based on quantum dots confined in optical cavities. For the reliable performance of the proposed QPE scheme constituting an arrangement of controlled-unitary gates, we evaluate the proposed quantum dot system under the effects of vacuum noise and leaky modes in an experimental implementation of the gates.

关键词： quantum phase estimation quantum algorithm Controlled-unitary gate quantum dot

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Leveraging Deep quantum Convolutional Neural Networks for Student Facial Expression Identifcation and Mode Assessments 4

Leveraging Deep Quantum Convolutional Neural Networks for St...

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4th International Conference on Ubiquitous Computing and Intelligent information Systems, ICUIS 2024

作者： Hossain, Javed Alam, Md. Nur Akib, Mohammad Yasin Nur Kogoya, Krisdahim Nanjing University of Information Science and Technology Dept. of Artificial Intelligence Nanjing China Nanjing University of Information Science and Technology Dept. of Computer Science and Technology Nanjing China

ISBN: (纸本)9798331529635

The incorporation of modern technologies into education has transformed how learning is seen and assessed, with a rising emphasis on understanding student behavior using facial expression recognition (FER). Recognizing students' emotional and cognitive states can provide vital insights into their engagement and learning styles, allowing teachers to adjust their teaching tactics in real time. Traditional machine learning algorithms have been used to solve this problem, but they frequently struggle with the complexity of facial expressions and big datasets, resulting in limitations in accuracy and processing efficiency. This study explores the potential of quantum computing in addressing these challenges through the development of a Deep quantum Convolutional Neural Network (QCNN) architecture. By combining the advanced image processing capabilities of convolutional neural networks (CNNs) with the computational power of quantum features such as superposition and entanglement, we aim to enhance the detection of facial expressions that reflect students' engagement, concentration, frustration, and satisfaction. Finally, the result demonstrates a classification accuracy of 91.58 © 2024 IEEE.

关键词： Convolutional neural networks

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Evolution and Applications of quantum Computing 1

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2023年

作者： Sachi Nandan Mohanty Rajanikanth Aluvalu Sarita Mohanty

EVOLUTION and APPLICATIONS of quantum COMPUTING The book is about the quantum Model replacing traditional computing’s classical model and gives a state-of-the-art technical overview of the current efforts to develop ... 详细信息

ISBN: (数字)9781119905172

ISBN: (纸本)9781119904861

关键词： quantum mechanics quantum teleportation single quantum bits quantum gates quantum computations quantum parallelism quantum algorithms density operators quantum state transformations and measurements quantum error correction fault tolerance and robust quantum computing information processing quantum independent error models bipartite quantum systems CSWAP quantum gates controlled phase gates holevo’s theorem unitary quantum gates quantum computation machine learning quantum computing quantum neural network quantum algorithms artificial intelligence virtual reality a-frame neural style transfer backpropagation zigbee module convolutional neural networks star topology phishing support vector machines logistics regression k-nearest neighbours random forest malicious emails malicious urls machine learning feature creation feature selection automl autoviz classification IDS visualization business intelligence supervised learning networking iot interface e-pharmacy digitalization quantum technology data processing service Industry quantum computing drug discovery computer-aided drug design artificial intelligence quantum circuit qubit

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