Article
Computer Science, Artificial Intelligence
Giovanni Acampora, Angela Chiatto, Autilia Vitiello
Summary: This paper discusses the application of quantum computing in optimization problems and proposes the use of genetic algorithms as gradient-free methods to optimize the parameters of Quantum Approximate Optimization Algorithm (QAOA) circuit. Experimental results on noisy quantum devices solving MaxCut problem show that the genetic algorithm outperforms other gradient-free optimizers in terms of approximation ratio.
APPLIED SOFT COMPUTING
(2023)
Article
Quantum Science & Technology
Christopher Chamberland, Luis Goncalves, Prasahnt Sivarajah, Eric Peterson, Sebastian Grimberg
Summary: Implementing algorithms on a fault-tolerant quantum computer requires fast decoding throughput and latency times to prevent exponential increase in buffer times. This study introduces the construction of local neural network decoders using three-dimensional convolutions, adapted to circuit-level noise. The application of these decoders removes errors and reduces syndrome density, allowing for accelerated implementation of global decoders. Strategies such as syndrome collapse and vertical cleanup further reduce syndrome density. The use of the local NN decoder and vertical cleanup strategy shows a significant speedup in the minimum-weight perfect matching decoder for a d=17 surface code volume. The cost of implementing these decoders on field programmable gate arrays is also estimated.
QUANTUM SCIENCE AND TECHNOLOGY
(2023)
Article
Computer Science, Interdisciplinary Applications
Davood Mohammadi, Mohamed Abd Elaziz, Reza Moghdani, Emrah Demir, Seyedali Mirjalili
Summary: This paper introduces an improved version of the Henry Gas Solubility Optimization (HGSO) algorithm called Quantum HGSO (QHGSO) algorithm, which applies quantum theory to enhance the balance between exploitation and exploration in solution space exploration. The QHGSO algorithm demonstrates superior performance in finding optimal solutions for global optimization functions and engineering problems compared to other well-known metaheuristic algorithms.
ENGINEERING WITH COMPUTERS
(2022)
Article
Chemistry, Multidisciplinary
Beimbet Daribayev, Aksultan Mukhanbet, Timur Imankulov
Summary: This article solves the 2D Poisson equation using the quantum algorithm HHL and showcases the materials and methods employed for the solution. The results demonstrate the low error rate of the quantum algorithm when solving the Poisson equation.
APPLIED SCIENCES-BASEL
(2023)
Article
Computer Science, Artificial Intelligence
Giovanni Acampora, Roberto Schiattarella, Autilia Vitiello
Summary: The paper proposes a quantum algorithm called Quantum Genetic Sampling (QGS) to increase population diversity and reduce the possibility of convergence to low-quality solutions in genetic evolution.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Review
Computer Science, Interdisciplinary Applications
Jaiteg Singh, Kamalpreet Singh Bhangu
Summary: This study aims to develop a clear understanding of the promises and limitations of the current state-of-the-art quantum computing use cases and to define directions for future research. It bridges the gap between computer professionals and non-physicists by offering conceptual and notational information and surveys existing applications, technological advancements, and contemporary challenges associated with quantum computing.
ARCHIVES OF COMPUTATIONAL METHODS IN ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Giovanni Acampora, Ferdinando Di Martino, Alfredo Massa, Roberto Schiattarella, Autilia Vitiello
Summary: This paper introduces the concept of Distributed Noisy-Intermediate Scale Quantum (D-NISQ) as a reference computational model to design innovative frameworks for quantum devices to interact and solve complex problems collaboratively. Through two case studies, a multi-threaded implementation of the D-NISQ model demonstrates greater reliability in solving problems through quantum computation.
INFORMATION FUSION
(2023)
Article
Multidisciplinary Sciences
Joshua J. Goings, Alec White, Joonho Lee, Christofer S. Tautermann, Matthias Degroote, Craig Gidney, Toru Shiozaki, Ryan Babbush, Nicholas C. Rubin
Summary: An accurate assessment of the potential computational advantages of quantum computers in chemical simulation is crucial for their deployment. This study explores the resources required for assessing the electronic structure of cytochrome P450 enzymes using quantum and classical computations, defining a boundary for classical-quantum advantage. The results show that simulation of large-scale CYP models has the potential to be a quantum advantage problem, emphasizing the interplay between classical computations and quantum algorithms in chemical simulation.
PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
(2022)
Article
Engineering, Multidisciplinary
Hoda Zamani, Mohammad H. Nadimi-Shahraki, Amir H. Gandomi
Summary: This paper presents a novel bio-inspired algorithm called SMO, which mimics the behaviors of starlings during their stunning murmuration, to solve complex engineering optimization problems. The SMO introduces dynamic multi-flock construction and three new search strategies, achieving competitive results in solution quality and convergence rate compared to other state-of-the-art algorithms.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2022)
Article
Computer Science, Information Systems
Giovanni Acampora, Autilia Vitiello
Summary: This study introduces a new evolutionary algorithm utilizing an actual quantum processor, which employs quantum phenomena to achieve significant speed-up in computation. By implementing quantum concepts such as quantum chromosome and entangled crossover, the proposed algorithm efficiently executes genetic evolution on quantum devices to converge towards proper sub-optimal solutions of a given optimization problem. The experimental results show that the synergy between quantum and evolutionary computation leads to a promising bio-inspired optimization strategy.
INFORMATION SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
Mohammed Zidan, Salem F. Hegazy, Mahmoud Abdel-Aty, Salah S. A. Obayya
Summary: We propose a quantum computation algorithm that can solve the problem of logical equivalence verification exponentially faster than classical deterministic computation. By executing oracles of two evaluated functions, a common target qubit is obtained and interacts with an ancillary qubit, with the degree of entanglement serving as a reliable witness for logical equivalence. The quantum algorithm's steps number is inversely proportional to the square of the measured concurrence value's standard error epsilon 2, independent of the input size.
APPLIED SOFT COMPUTING
(2023)
Review
Chemistry, Multidisciplinary
Mario Motta, Julia E. Rice
Summary: Digital quantum computers provide a computational framework for solving the Schrodinger equation for many-particle systems, with recent remarkable growth in quantum computing algorithms for quantum simulation. This review introduces emerging algorithms for the simulation of Hamiltonian dynamics and eigenstates, focusing on their applications to electronic structure in molecular systems. Theoretical foundations, implementation details, strengths, limitations, and recent advances of the method are discussed.
WILEY INTERDISCIPLINARY REVIEWS-COMPUTATIONAL MOLECULAR SCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Alokeparna Choudhury, Sourav Samanta, Sanjoy Pratihar, Oishila Bandyopadhyay
Summary: Microscopic image segmentation is crucial for detecting and diagnosing diseases like Alzheimer's, kidney disease, and cancer. This study introduces an enhanced firefly algorithm-based segmentation method using quantum superposition and update operations, showing effective results in segmenting hippocampus images.
APPLIED INTELLIGENCE
(2022)
Article
Quantum Science & Technology
Pablo Diez-Valle, Jorge Luis-Hita, Senaida Hernandez-Santana, Fernando Martinez-Garcia, Alvaro Diaz-Fernandez, Eva Andres, Juan Jose Garcia-Ripoll, Escolastico Sanchez-Martinez, Diego Porras
Summary: In this paper, we propose a new method for solving combinatorial optimization problems with challenging constraints using Variational Quantum Algorithms (VQAs). We introduce the Multi-Objective Variational Constrained Optimizer (MOVCO) which updates the variational parameters by a classical multi-objective optimization performed by a genetic algorithm. We test this method on a real-world problem in finance and show significant improvement in terms of cost and the avoidance of local minima that do not satisfy mandatory constraints.
QUANTUM SCIENCE AND TECHNOLOGY
(2023)
Article
Materials Science, Multidisciplinary
Nahum Sa, Ivan S. Oliveira, Itzhak Roditi
Summary: In this work, we use a NISQ framework to obtain the gap of a BCS Hamiltonian, which has implications for superconductivity research. We choose to use Variational Quantum Deflation and analyze hardware restrictions for finding energy spectra on current quantum hardware. We compare COBYLA and SPSA classical optimizers, and study the effects of noise-induced decoherence in simulations on real devices. We apply our method to examples with 2 and 5 qubits, and show how to approximate the gap within one standard deviation, even with noise present.
RESULTS IN PHYSICS
(2023)
Article
Automation & Control Systems
Xiaotong Li, Licheng Jiao, Hao Zhu, Fang Liu, Shuyuan Yang, Xiangrong Zhang, Shuang Wang, Rong Qu
Summary: This article proposes a collaborative learning tracking network for remote sensing videos, which includes CRFPF module, DSCA module, and GCRT strategy. Experimental results demonstrate the accuracy and effectiveness of this method in complex remote sensing scenes.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Automation & Control Systems
Zhu Xiao, Hui Fang, Hongbo Jiang, Jing Bai, Vincent Havyarimana, Hongyang Chen, Licheng Jiao
Summary: This article proposes a deep learning framework, STANet-NALU, to understand the dynamic aggregation effect of private cars on weekends. It improves the kernel density estimator and utilizes the stay time of private cars as temporal features, and designs a spatiotemporal attention module and a gate control unit for adaptive feature fusion. The experiments show that STANet-NALU outperforms existing methods in predicting the aggregation effect.
IEEE TRANSACTIONS ON CYBERNETICS
(2023)
Article
Computer Science, Artificial Intelligence
Wenping Ma, Yating Li, Hao Zhu, Haoxiang Ma, Licheng Jiao, Jianchao Shen, Biao Hou
Summary: This article discusses how to design a network by utilizing the characteristics of panchromatic images (PANs) and multispectral images (MSs), and proposes a multi-scale progressive collaborative attention network (MPCA-Net). The study improves the adaptive dilation rate selection strategy and introduces a center pixel migration (CPM) strategy to address excessive scale differences and information overlap. Additionally, different modules are carefully designed for each feature extraction stage of the two branches to account for their distinct spatial and spectral characteristics. Experimental results demonstrate the competitive performance of the proposed method.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jie Gao, Licheng Jiao, Fang Liu, Shuyuan Yang, Biao Hou, Xu Liu
Summary: In this article, a novel classification framework called MSCCN is proposed, which effectively represents image features using a multiscale curvelet-scattering module (CCM). By utilizing multiscale geometric analysis and curvelet features, the framework improves the feature representation and achieves better classification accuracy.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Jun Zhang, Licheng Jiao, Wenping Ma, Fang Liu, Xu Liu, Lingling Li, Hao Zhu
Summary: This article proposes a regularized descriptor learning network (RDLNet) that focuses on the learning of hard samples and compact descriptor representation. It introduces a novel hard sample mining strategy and a batch margin loss function to optimize the distance of extreme cases. The proposed RDLNet provides a compact and discriminative low-dimensional representation with significant improvements in various scenarios.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Review
Computer Science, Artificial Intelligence
Licheng Jiao, Dan Wang, Yidong Bai, Puhua Chen, Fang Liu
Summary: In this article, we provide a comprehensive review of the development of deep learning in visual tracking, including deep feature representations, network architecture, and key issues. We also analyze the performance of DL-based approaches and propose future directions and tasks.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Environmental Sciences
Yanqiao Chen, Yangyang Li, Heting Mao, Guangyuan Liu, Xinghua Chai, Licheng Jiao
Summary: Remote sensing image scene classification (RSISC) has attracted significant attention in recent years. Deep learning methods have shown promising performance in classifying remote sensing images (RSI), but they usually require a large amount of labeled data. Acquiring sufficient labeled data is costly, making few-shot RSISC highly meaningful. In this study, we propose a discriminative enhanced attention-based deep nearest neighbor neural network (DEADN4) to address the few-shot RSISC task. Our approach introduces center loss, deep local-global descriptors (DLGD), and modifies the Softmax loss with cosine margin. Experimental results on diverse RSI datasets demonstrate the efficacy of our approach compared to state-of-the-art methods.
Article
Environmental Sciences
Tianyi Zhang, Chenhao Qin, Weibin Li, Xin Mao, Liyun Zhao, Biao Hou, Licheng Jiao
Summary: In this study, we proposed a new method (MF-SegFormer) for automatically extracting water bodies from remote sensing images in complex environments. The method, which combines multiscale fusion and feature enhancement techniques, performs well in extracting small water bodies and water body edge information, and its superiority is demonstrated through comparisons with other methods.
Article
Computer Science, Artificial Intelligence
Xiao Liu, Fanjin Zhang, Zhenyu Hou, Li Mian, Zhaoyu Wang, Jing Zhang, Jie Tang
Summary: Deep supervised learning has been successful, but it is limited by manual labels and vulnerable to attacks. In contrast, self-supervised learning utilizes input data as supervision, showing promising performance on representation learning. This survey comprehensively reviews self-supervised learning methods in computer vision, natural language processing, and graph learning.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Geochemistry & Geophysics
Xianghai Cao, Haifeng Lin, Shuaixu Guo, Tao Xiong, Licheng Jiao
Summary: This article introduces a Transformer-based masked autoencoder method using contrastive learning (TMAC) to solve the problem of lacking accurately labeled hyperspectral image data. By combining the methods of contrastive learning and masked autoencoder, this approach demonstrates powerful feature extraction capability and achieves outstanding results in hyperspectral image classification.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Computer Science, Artificial Intelligence
Hongying Liu, Zhijin Ge, Zhenyu Zhou, Fanhua Shang, Yuanyuan Liu, Licheng Jiao
Summary: In this article, the authors focus on the properties of the activation function ReLU and propose a universal gradient correction method called ADV-ReLU to enhance the performance of gradient-based white-box attack algorithms. Experimental results demonstrate that ADV-ReLU can be easily integrated into state-of-the-art algorithms and transferred to black-box attacks to decrease perturbations.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Geochemistry & Geophysics
Jing Chen, Biao Hou, Bo Ren, Qian Wu, Licheng Jiao
Summary: This paper proposes a PolSAR classification algorithm based on the Wishart locally constrained expansion (WLCE) algorithm, combined with convolutional neural network and Markov random field for training and post-processing. Experimental results show that the algorithm outperforms state-of-the-art algorithms on several benchmark datasets.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Geochemistry & Geophysics
Jie Feng, Zizhuo Gao, Ronghua Shang, Xiangrong Zhang, Licheng Jiao
Summary: Generative adversarial network (GAN) and its variants provide a powerful training mechanism for hyperspectral image (HSI) classification. However, the single-generation pattern of GANs tends to collapse for HSI sample generation, and the performance of the generator is limited. To address these issues, a multi-complementary GANs with contrastive learning (CMC-GAN) is proposed, which generates diverse multiscale samples by introducing multiple groups of GANs and a contrastive learning constraint, leading to superior classification performance.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2023)
Article
Computer Science, Artificial Intelligence
Jie Chen, Licheng Jiao, Xu Liu, Fang Liu, Lingling Li, Shuyuan Yang
Summary: This article proposes a novel approach for modeling contextual relationships in images using a multiresolution interpretable contourlet graph network (MICGNet), which balances graph representation learning with the geometric features of images. Experimental analysis shows that MICGNet is significantly more effective and efficient than other recent algorithms.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Artificial Intelligence
Yunpeng Li, Xiangrong Zhang, Xina Cheng, Xu Tang, Licheng Jiao
Summary: Tremendous progresses have been made in remote sensing image captioning (RSIC) task in recent years. This work focuses on injecting high-level visual-semantic interaction into RSIC model. The experiments on three benchmark data sets show the superiority of our approach compared with the reference methods.
PATTERN RECOGNITION
(2024)
