The quantum Internet is profoundly impacting the world and is expected to be operational in the future. With the global wide-area quantum networks begin to take shape, drone-based quantum entanglement networks have attracted widespread attention. Compared to quantum links based on optical fibers or satellites, drone-based quantum entanglement networks can effectively address the need for responsive and on-demand quantum network coverage across diverse locations and operational times. To realize network construction, entanglement routing protocols pose a significant challenge. On the one hand, the fidelity of the entangled system degrades hop-byhop. On the other hand, the dynamics and limited energy of drone nodes are equally not negligible. However, existing works have not adequately addressed these issues. In this paper, we present a novel fidelity-guaranteed entanglement routing protocol for drone-based quantum networks (FGER_D). The FGER_D considers the node energy and quantum link vulnerability comprehensively, selects end-to-end fidelity as the routing metric, and employs multiple attempts' entanglement purification scheme to provide fidelity-guaranteed entanglement connections in noisy environments. Numerical simulations demonstrate that compared to existing algorithms, FGER_D improves network throughput by approximately 24% under default settings, while also ensuring network fidelity and extending network lifetime. Our work is expected to provide a theoretical basis for the deployment of drone-based entanglement distribution networks.
{"title":"Entanglement routing with guaranteed fidelity for drone-based quantum networks","authors":"Haoran Hu;Huazhi Lun;Ya Wang;Zhifeng Deng;Jiahao Li;Jie Tang;Yuexiang Cao;Ying Liu;Dan Wu;Huicun Yu;Xingyu Wang;Jiahua Wei;Lei Shi","doi":"10.23919/JCN.2025.000046","DOIUrl":"https://doi.org/10.23919/JCN.2025.000046","url":null,"abstract":"The quantum Internet is profoundly impacting the world and is expected to be operational in the future. With the global wide-area quantum networks begin to take shape, drone-based quantum entanglement networks have attracted widespread attention. Compared to quantum links based on optical fibers or satellites, drone-based quantum entanglement networks can effectively address the need for responsive and on-demand quantum network coverage across diverse locations and operational times. To realize network construction, entanglement routing protocols pose a significant challenge. On the one hand, the fidelity of the entangled system degrades hop-byhop. On the other hand, the dynamics and limited energy of drone nodes are equally not negligible. However, existing works have not adequately addressed these issues. In this paper, we present a novel fidelity-guaranteed entanglement routing protocol for drone-based quantum networks (FGER_D). The FGER_D considers the node energy and quantum link vulnerability comprehensively, selects end-to-end fidelity as the routing metric, and employs multiple attempts' entanglement purification scheme to provide fidelity-guaranteed entanglement connections in noisy environments. Numerical simulations demonstrate that compared to existing algorithms, FGER_D improves network throughput by approximately 24% under default settings, while also ensuring network fidelity and extending network lifetime. Our work is expected to provide a theoretical basis for the deployment of drone-based entanglement distribution networks.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 4","pages":"241-251"},"PeriodicalIF":3.2,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11142614","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904870","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-26DOI: 10.23919/JCN.2025.000008
Vuppula Roopa;Himansu Shekhar Pradhan
Future wireless communication networks are exploring the 0.1 to 10 terahertz (THz) band, which presents opportunities for creative usage. However, the management of growing privacy and security issues while allocating resources efficiently to support many devices is a critical activity. Complicated technology affects resource allocation (RA) and network management as it permeates devices and infrastructure. Upgrading from fifth-generation (5G) to next-generation represents breakthroughs in ultra-low latency, fast data speeds, and artificial intelligence (AI) integration for innovative services and applications. However, these developments convey the challenges that include data processing, RA, network administration, and privacy. Integrating blockchain (BC) as well as machine learning (ML) is a potential alternative to tackle these challenges. This paper presents a comprehensive review, which explores their combined contributions to trust, decentralization, and network security in ML decisions, immutability, and streamlined model sharing. Moreover, it delves into various areas such as rate splitting, next-generation radaroriented communication, BC-oriented spectrum reframing, reconfigurable intelligent surfaces (RIS), and integrated sensing and communication. In addition, it investigates using ML and BC in emerging next-generation communication technologies, which include semantic, molecular, and holographic communications. Finally, the authors deal with the essential unsolved issues, challenges, prospective solutions, and the wide range of opportunities for additional research in this rapidly evolving fields.
{"title":"Exploring blockchain and artificial intelligence for next generation wireless networks","authors":"Vuppula Roopa;Himansu Shekhar Pradhan","doi":"10.23919/JCN.2025.000008","DOIUrl":"https://doi.org/10.23919/JCN.2025.000008","url":null,"abstract":"Future wireless communication networks are exploring the 0.1 to 10 terahertz (THz) band, which presents opportunities for creative usage. However, the management of growing privacy and security issues while allocating resources efficiently to support many devices is a critical activity. Complicated technology affects resource allocation (RA) and network management as it permeates devices and infrastructure. Upgrading from fifth-generation (5G) to next-generation represents breakthroughs in ultra-low latency, fast data speeds, and artificial intelligence (AI) integration for innovative services and applications. However, these developments convey the challenges that include data processing, RA, network administration, and privacy. Integrating blockchain (BC) as well as machine learning (ML) is a potential alternative to tackle these challenges. This paper presents a comprehensive review, which explores their combined contributions to trust, decentralization, and network security in ML decisions, immutability, and streamlined model sharing. Moreover, it delves into various areas such as rate splitting, next-generation radaroriented communication, BC-oriented spectrum reframing, reconfigurable intelligent surfaces (RIS), and integrated sensing and communication. In addition, it investigates using ML and BC in emerging next-generation communication technologies, which include semantic, molecular, and holographic communications. Finally, the authors deal with the essential unsolved issues, challenges, prospective solutions, and the wide range of opportunities for additional research in this rapidly evolving fields.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 5","pages":"386-411"},"PeriodicalIF":3.2,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11142611","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145533048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-26DOI: 10.23919/JCN.2025.000066
{"title":"Information for authors","authors":"","doi":"10.23919/JCN.2025.000066","DOIUrl":"https://doi.org/10.23919/JCN.2025.000066","url":null,"abstract":"","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 4","pages":"274-278"},"PeriodicalIF":3.2,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11142606","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
It is imperative to reduce carbon dioxide emissions and fossil fuel consumption to ensure the Earth's sustainable future and mitigate negative impacts like extreme climate changes caused by greenhouse gases. Electric vehicles (EVs) play a pivotal role in this context, where the convenience of charging stations is crucial in influencing consumer choices. This paper formulates the placement of charging stations as an optimization problem, considering factors like station density, accessibility, coverage, and regional cost variations, to minimize total construction costs. The problem is mapped onto a quadratic unconstrained binary optimization (QUBO) model. The QUBO can efficiently represent and solve complex combinatorial optimization problems, making it suitable for quantum and advanced classical algorithms. Utilizing digital quantum annealing, the optimal or near-optimal solutions are efficiently identified. The performance of the proposed approach is compared with classical algorithms, including mixed linear programming and simulated annealing, demonstrating significant advancements of digital annealing.
{"title":"Solving optimal electric vehicle charging station placement problem using digital quantum annealing","authors":"Chia-Ho Ou;Chung-Chieh Cheng;Chih-Yu Chen;Krischonme Bhumkittipich;Sillawat Romphochai","doi":"10.23919/JCN.2025.000048","DOIUrl":"https://doi.org/10.23919/JCN.2025.000048","url":null,"abstract":"It is imperative to reduce carbon dioxide emissions and fossil fuel consumption to ensure the Earth's sustainable future and mitigate negative impacts like extreme climate changes caused by greenhouse gases. Electric vehicles (EVs) play a pivotal role in this context, where the convenience of charging stations is crucial in influencing consumer choices. This paper formulates the placement of charging stations as an optimization problem, considering factors like station density, accessibility, coverage, and regional cost variations, to minimize total construction costs. The problem is mapped onto a quadratic unconstrained binary optimization (QUBO) model. The QUBO can efficiently represent and solve complex combinatorial optimization problems, making it suitable for quantum and advanced classical algorithms. Utilizing digital quantum annealing, the optimal or near-optimal solutions are efficiently identified. The performance of the proposed approach is compared with classical algorithms, including mixed linear programming and simulated annealing, demonstrating significant advancements of digital annealing.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 4","pages":"252-263"},"PeriodicalIF":3.2,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11142623","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904867","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-26DOI: 10.23919/JCN.2025.000037
Haosu Cheng;Guanquan Shi;Kangkang Zhang
Blockchain, as a distributed ledger technology, embodies decentralization, security transparency, and traceability. Interoperability has been a persistent challenge in blockchain technology research. The inherent characteristics of blockchain, including numerous terminal nodes, diverse network architectures, cross-chain application services, and intricate network environments, present significant privacy and data security concerns for cross-chain gateways during data collection and transmission. To address these security challenges, this paper proposes a system architecture and security domain isolation model for cross-chain security gateways leveraging system resource virtualization. This architecture aims to address security issues encountered in the process of collecting, transmitting, and exchanging data elements within the cross-chain gateway. Specifically, the proposed architecture isolates the different layers in the cross-chain gateway by business domains, and ensures mutual security isolation between distinct business domains and network domains during cross-chain activities. Moreover, the paper implements a secure gateway operational environment utilizing x86 hardware platforms and virtualization technology, followed by simulation experiment and performance testing to validate the architectural feasibility. Formal proof is provided to establish the security of the proposed domain model.
{"title":"CrossGuardian: A security domain isolation model for cross-chain gateway","authors":"Haosu Cheng;Guanquan Shi;Kangkang Zhang","doi":"10.23919/JCN.2025.000037","DOIUrl":"https://doi.org/10.23919/JCN.2025.000037","url":null,"abstract":"Blockchain, as a distributed ledger technology, embodies decentralization, security transparency, and traceability. Interoperability has been a persistent challenge in blockchain technology research. The inherent characteristics of blockchain, including numerous terminal nodes, diverse network architectures, cross-chain application services, and intricate network environments, present significant privacy and data security concerns for cross-chain gateways during data collection and transmission. To address these security challenges, this paper proposes a system architecture and security domain isolation model for cross-chain security gateways leveraging system resource virtualization. This architecture aims to address security issues encountered in the process of collecting, transmitting, and exchanging data elements within the cross-chain gateway. Specifically, the proposed architecture isolates the different layers in the cross-chain gateway by business domains, and ensures mutual security isolation between distinct business domains and network domains during cross-chain activities. Moreover, the paper implements a secure gateway operational environment utilizing x86 hardware platforms and virtualization technology, followed by simulation experiment and performance testing to validate the architectural feasibility. Formal proof is provided to establish the security of the proposed domain model.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 5","pages":"310-326"},"PeriodicalIF":3.2,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11142616","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145533042","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-26DOI: 10.23919/JCN.2025.000034
Junaid Farooq;Unnikrishna Pillai
The analysis of coverage probability based on signal-to-interference-plus-noise-ratio (SINR) is a classical problem in the study of wireless and cellular networks. Stochastic geometry (SG) has opened up the possibility of accurate coverage characterization for random spatial deployment of base stations. While results obtained using SG are tractable and compact, in most cases, they are usually in the form of incomplete integrals, which need to be efficiently computed. Although that is possible with the computational capabilities available today, it masks the underlying structure in the analysis precluding the possibility of using it for solving optimization and system design problems. This paper provides an alternate approach to analyzing SINR-based coverage probability using direct probability computation. We analyze a uniformly random wireless network as a test case and compare the analytical results with widely accepted frameworks in the SG literature. Our analytical derivations, validated through simulation studies, agree with well-known results in the literature. The developed approach provides the groundwork for coverage analysis in more complex network scenarios and channel conditions.
{"title":"A probabilistic alternative to coverage analysis in uniform random wireless networks","authors":"Junaid Farooq;Unnikrishna Pillai","doi":"10.23919/JCN.2025.000034","DOIUrl":"https://doi.org/10.23919/JCN.2025.000034","url":null,"abstract":"The analysis of coverage probability based on signal-to-interference-plus-noise-ratio (SINR) is a classical problem in the study of wireless and cellular networks. Stochastic geometry (SG) has opened up the possibility of accurate coverage characterization for random spatial deployment of base stations. While results obtained using SG are tractable and compact, in most cases, they are usually in the form of incomplete integrals, which need to be efficiently computed. Although that is possible with the computational capabilities available today, it masks the underlying structure in the analysis precluding the possibility of using it for solving optimization and system design problems. This paper provides an alternate approach to analyzing SINR-based coverage probability using direct probability computation. We analyze a uniformly random wireless network as a test case and compare the analytical results with widely accepted frameworks in the SG literature. Our analytical derivations, validated through simulation studies, agree with well-known results in the literature. The developed approach provides the groundwork for coverage analysis in more complex network scenarios and channel conditions.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 5","pages":"282-297"},"PeriodicalIF":3.2,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11142613","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145533050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-26DOI: 10.23919/JCN.2025.000045
Hye Yeong Lee;Man Hee Lee;Soo Young Shin
Quantum neural networks (QNNs) have attracted significant attention recently, primarily because of their potential to address complex problems deemed difficult for traditional computational methods. This study explores the viability of QNN in handling multiclass classification tasks in downlink nonorthogonal multiple access (NOMA) frameworks. The investigation includes a design of QNN framework and performance evaluation of a QNN-based NOMA detector, integrating maximum likelihood (ML), successive interference cancellation (SIC), and rotated ML (RML) methods. A QNN framework was configured for all three detectors, and a comparative analysis was conducted in terms of loss, accuracy, and testing across varied signal-to-noise ratio (SNR) levels and power allocation coefficients, considering NOMA-specific characteristics. Furthermore, the computational complexity of each detector was analyzed within the proposed framework.
{"title":"QNN framework based multiclass classification for downlink NOMA detectors","authors":"Hye Yeong Lee;Man Hee Lee;Soo Young Shin","doi":"10.23919/JCN.2025.000045","DOIUrl":"https://doi.org/10.23919/JCN.2025.000045","url":null,"abstract":"Quantum neural networks (QNNs) have attracted significant attention recently, primarily because of their potential to address complex problems deemed difficult for traditional computational methods. This study explores the viability of QNN in handling multiclass classification tasks in downlink nonorthogonal multiple access (NOMA) frameworks. The investigation includes a design of QNN framework and performance evaluation of a QNN-based NOMA detector, integrating maximum likelihood (ML), successive interference cancellation (SIC), and rotated ML (RML) methods. A QNN framework was configured for all three detectors, and a comparative analysis was conducted in terms of loss, accuracy, and testing across varied signal-to-noise ratio (SNR) levels and power allocation coefficients, considering NOMA-specific characteristics. Furthermore, the computational complexity of each detector was analyzed within the proposed framework.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 4","pages":"231-240"},"PeriodicalIF":3.2,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11142625","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-26DOI: 10.23919/JCN.2025.000067
{"title":"Open access publishing agreement","authors":"","doi":"10.23919/JCN.2025.000067","DOIUrl":"https://doi.org/10.23919/JCN.2025.000067","url":null,"abstract":"","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 4","pages":"279-281"},"PeriodicalIF":3.2,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11142621","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-26DOI: 10.23919/JCN.2025.000021
Emily Jimin Roh;Soohyun Park;Soyi Jung;Joongheon Kim
Real-time processing with high classification accuracy is a fundamental requirement in autonomous driving systems. However, existing neural network models for classification often face a tradeoff between computational efficiency and accuracy, necessitating the development of advanced optimization methods to address this limitation. Additionally, dynamic driving environments offer opportunities to enhance classification performance by leveraging the principles of quantum computing, particularly the properties of superposition and entanglement. In response to these challenges, a multi-stage quantum convolutional neural network (MS-QCNN) approach is proposed, designed to improve image analysis performance by effectively utilizing the multi-stage structure of QCNN. A Lyapunov optimization framework is applied to achieve optimal performance, which maximizes time-averaged efficiency while ensuring system stability. This framework dynamically adjusts the MS-QCNN model in response to environmental variations, promoting enhanced queue stability and achieving optimal time-averaged performance.
{"title":"Stabilized classification control using multi-stage quantum convolutional neural networks for autonomous driving","authors":"Emily Jimin Roh;Soohyun Park;Soyi Jung;Joongheon Kim","doi":"10.23919/JCN.2025.000021","DOIUrl":"https://doi.org/10.23919/JCN.2025.000021","url":null,"abstract":"Real-time processing with high classification accuracy is a fundamental requirement in autonomous driving systems. However, existing neural network models for classification often face a tradeoff between computational efficiency and accuracy, necessitating the development of advanced optimization methods to address this limitation. Additionally, dynamic driving environments offer opportunities to enhance classification performance by leveraging the principles of quantum computing, particularly the properties of superposition and entanglement. In response to these challenges, a multi-stage quantum convolutional neural network (MS-QCNN) approach is proposed, designed to improve image analysis performance by effectively utilizing the multi-stage structure of QCNN. A Lyapunov optimization framework is applied to achieve optimal performance, which maximizes time-averaged efficiency while ensuring system stability. This framework dynamically adjusts the MS-QCNN model in response to environmental variations, promoting enhanced queue stability and achieving optimal time-averaged performance.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 4","pages":"264-272"},"PeriodicalIF":3.2,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11142618","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904869","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-08-26DOI: 10.23919/JCN.2025.000038
Youngjin Seo;Jun Heo
The Quantum Approximate Optimization Algorithm (QAOA) is a promising framework for combinatorial optimization, yet its performance is often hindered by the complexity of parameter optimization. In this work, we investigate phase relationships in Grover-QAOA (G-QAOA) for solving 3-SAT problems and introduce a novel phase matching condition that simplifies the optimization landscape. By aligning the phases of the problem and mixing Hamiltonians, our approach reduces the number of variational parameters from 2p to p, significantly lowering computational overhead. We further propose single-angle G-QAOA, an extension that enables additional parameter reduction. Numerical simulations demonstrate that our method achieves success probabilities comparable to those of standard G-QAOA while requiring fewer quantum circuit evaluations. These results highlight the potential of our proposed G-QAOA for practical implementation on near-term quantum hardware.
{"title":"Phase matching in Grover-QAOA for solving 3-SAT problems","authors":"Youngjin Seo;Jun Heo","doi":"10.23919/JCN.2025.000038","DOIUrl":"https://doi.org/10.23919/JCN.2025.000038","url":null,"abstract":"The Quantum Approximate Optimization Algorithm (QAOA) is a promising framework for combinatorial optimization, yet its performance is often hindered by the complexity of parameter optimization. In this work, we investigate phase relationships in Grover-QAOA (G-QAOA) for solving 3-SAT problems and introduce a novel phase matching condition that simplifies the optimization landscape. By aligning the phases of the problem and mixing Hamiltonians, our approach reduces the number of variational parameters from 2p to p, significantly lowering computational overhead. We further propose single-angle G-QAOA, an extension that enables additional parameter reduction. Numerical simulations demonstrate that our method achieves success probabilities comparable to those of standard G-QAOA while requiring fewer quantum circuit evaluations. These results highlight the potential of our proposed G-QAOA for practical implementation on near-term quantum hardware.","PeriodicalId":54864,"journal":{"name":"Journal of Communications and Networks","volume":"27 4","pages":"222-230"},"PeriodicalIF":3.2,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11142622","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144904871","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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