Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

International Conference on Pioneering Developments in Computer Science & Digital Technologies (IC2SDT)

Quantum-Aided Low-Latency Network for Intelligent Reflecting Surfaces in Optical Communication

Kritika Upadhyay, Manisha Bharti, Manoj Kumar

Published Dec 4, 2025• 0 citations
Publisher

Abstract

To achieve the goal of a seamless-connected world with communication assistance, quantum technology is crucial. However, the key limitations are the limited availability of network resources and regulated radio frequency spectrum for future commitments. Future technology will enable billions of connected devices to communicate smoothly, manage data effectively, and achieve computational accuracy. Designing an intelligently operated system with dynamic networks can only offer expanded network capabilities, which is necessary. Quantum computing is a fascinating use of quantum physics with broad implications. Quantum computing, which processes information using quantum bits (qubits), has fundamentally altered how humans process and send information. This study analyses the integration of an Intelligent Reflecting Surface (IRS) with quantum computing with a hybrid Nonorthogonal Multiple Access (NOMA) technique. It shows an increased sum rate of 28 bps/Hz (an improvement of 33%) and a minimal outage probability. To understand the properties of quantum states, Wigner function plots are analysed for three cat states: even, odd, and compass. Thus, the suggested framework is suitable for the upcoming next-generation wireless technologies.

DOI
10.1109/ic2sdt68218.2025.11383620

Related papers

Resource Allocation Optimization by Quantum Computing for Shared Use of Standalone IRS

Takahiro Ohyama, Yuichi Kawamoto, Nei Kato

Intelligent reflecting surfaces (IRSs) have attracted attention as a technology that can considerably improve the energy utilization efficiency of sixth-generation (6G) mobile communication systems. IRSs enable control of propagation characteristics by adjusting the phase shift of each reflective element. However, designing the phase shift requires the acquisition of channel information for each reflective element, which is impractical from an overhead perspective. In addition, for multiple wireless network operators to share an IRS for communication, new infrastructure facilities and operational costs are required at each operator's end to control the IRS in a coordinated manner. Herein, we propose a wireless communication system using standalone IRSs to solve these problems. The standalone IRSs cover a wide area by periodically switching phase shifts, and each operator allocates radio resources according to their phase-shift switching. Furthermore, we derive a quadratic unconstrained binary optimization equation for the proposed system to optimize radio resource allocation using quantum computing. The results of computer simulations indicate that the proposed system and method can be used to achieve efficient communication in 6G mobile communication systems.

IEEE Transactions on Emerging Topics in Computing•Jul 11, 2023•22 citations

An overview of quantum computing and quantum communication systems

Shahid Mumtaz, Mohsen Guizani

While the commercial deployment of 5G networks and beyond is a reality, the first work for the definition and study of 6G networks has started. The scientific community agrees that these networks will be characterised by a very high spatial density of access points, heterogeneity of access technologies, an increased number of users per access point, and demand for ubiquitous connectivity that must combine ultra-low latency, very high bandwidth, and high energy efficiency [1, 2]. Among the emerging challenges, holographic communications, high-precision manufacturing, the ubiquitous introduction of intelligence, and the incorporation of new technologies based on sub-terahertz (THz) or Visible Light Communications (VLC) are real issues. These are taking place in a truly three-dimensional coverage framework, integrating terrestrial and aerial radio to meet the needs with cloud-based capabilities where and when needed (on-demand). Radio frequencies are used in wireless telecommunications, but the need for very high throughput requires wider bandwidths, hence very high frequencies, in particular THz bands. Moving to a higher frequency range—from 100 GHz to 10 THz—is expected to significantly increase the bandwidth of the radio channel, which will make it possible to serve a significant number of users. In this case, we are not talking about the connection of cell phones, tablets, or computers (and even smart cars)—we are considering the use of Internet of Things (IoT) devices, which within one base station can be quite a lot. Therefore, the technologies for beamforming, device location, etc., developed for the 5G generation that is just being implemented now should also remain but will be used at higher frequencies [3]. For which 6G is primarily intended, IoT solutions have been given a particular name: ‘human-machine-things’. They involve three elements in the system: a person as a physical carrier; an intelligent device with which the person interacts; collects data and executes commands from an application running on the person's device [4]. The 6G radio networks will provide the means of communication and data gathering necessary to accumulate information. Still, a system's approach will be required for the 6G technology market as a whole involving data analytics, artificial intelligence (AI), and next-generation computation capabilities via HPC and quantum computing [3, 5]. This tremendous amount of data may be harnessed, with strong processing and learning capabilities, to manage the network at different levels. To this end, quantum computing methods can play a significant enabling role and can provide a guaranteed security platform. Towards provisioning this massive connectivity and efficiently processing the voluminous data available at the user and network sides, quantum-powered computing methods have a strong potential in realising the ambitions of a service-driven, fully intelligent 6G communication network. There is every reason to believe that the integration of quantum technology can improve the throughput, efficiency, and security of 6G networks. In addition to the advantages mentioned above, this technology also has benefits in computing speed, guaranteed security, and minimal storage requirements. This makes it ideally suited for various future quantum communication network applications. Research is needed on many fronts to evaluate the potential opportunities of quantum computing in 6G applications. In 5G, a functional division is operated in both the RAN and core networks to meet the ultra-low latency and ultra-high efficiency requirements of 5G verticals. Notably, RAN functions are split into cloud-deployed services with interfacing capabilities. This new architecture has a significant impact on the efficient implementation of security processes [6]. One of the key challenges in deploying secure 5G networks is analysing how current cryptographic algorithms for secure authentication and data transmission can adapt to provide secure authentication between services and secure data encryption while ensuring that stringent efficiency requirements are met [7]. The powerful parallel processing capability of quantum search algorithms can be leveraged to solve large-scale wireless optimisation problems. (1) Is this ‘beyond 5G?’ This topic is highly exploratory and undoubtedly beyond the boundaries of core 5G R&D. (2) What are the new features and key challenges? Classically, the mutual information between the channel input and output must be maximised. For quantum channels, the capacity must be redefined. Quantum channels can increase the achievable capacity. As a result, error correction without redundancy is possible on noisy channels. Quantum computers can enable exponentially complex combinatorial ML decoding in classical systems [5, 6]. To cope with latency-critical scenarios such as automated cars and medical applications, communication must be accomplished in a concise time. Achieving latency of only a few milliseconds is a real challenge. To achieve low latency and ultra-high reliability, it is essential to develop powerful high-end processors that consume little power. Quantum computing seems to be a reliable solution to this issue [8]. With variable quality of service (QoS) requirements, a variable radio resource must be allocated to the user. This can be of variable bandwidth, variable power, or both. Another challenging aspect of 6G is that with the propagation frequency, the signal can attenuate quickly and have a high penetration loss at high frequencies [9]. The signal is also automatically attenuated when accessing homes, residences, or workplaces. As the frequency increases, radio waves attenuate and may have difficulty penetrating the walls of buildings and homes while affecting users' QoS requirements. Therefore, it is very important to develop stable, fast and accurate quantum algorithms to handle 6G communication requirements by dynamically allocating variable resources [10]. The current 5G standard does not address the issue of quantum computing but relies on traditional cryptography. The development towards cloud and edge native infrastructures is expected to continue in 6G networks. While large-scale quantum computing can be expected to take a longer duration, it is time to prepare for the shift to cryptography that is secure in the post-quantum world. According to current knowledge, contemporary symmetric cryptography remains secure for the most part, even after the advent of quantum computing. Furthermore, authentication by a physical layer signature, such as RF fingerprinting, and some other technologies, such as randomisation of MIMO transmission coefficients, coding, etc., could potentially be used in 6G [5, 11]. Whether it is for the optimisation of heterogeneous resources in 6G networks that result in the solution of ‘NP-complete’ combinatorial problems, for the compensation of non-linear effects of a communication chain or the optimisation of MIMO transmitter pre-coding matrix, future network nodes and devices will have to be equipped with learning and decision-making capabilities through AI and machine learning, so that they autonomously optimise their operation in response to changes in the environment [5]. In this context, the use of optimisation techniques based on quantum computing and AI applied to the field of 6G networks could be of considerable interest. Besides, quantum computing allows NP-hard optimisation problems to be solved in linear time, making it possible to solve and execute network optimisation problems in a much faster time, even in real-time [5, 11]. Regulatory and standardisation work will impact our future technology solutions in 5G networks and beyond. Protecting individual privacy and identity has long been a challenge for standards bodies, as countries have very different perspectives and regulations in this area. International standards bodies such as JTC 1 have committees working on privacy frameworks and are making progress, but there is still much to be done [13]. To conclude, we believe that our readers will find this opinion piece useful in giving an overview of the importance of Quantum Computing/communications in shaping the next communication systems and its security platforms. This is a technology in its infancy and a lot of research remains to be carried out to achieve sustainable solutions and overcome the current challenges. The use of intelligent systems (AI, ML and other techniques) will be essential in achieving solutions faster and providing sustainable outcomes. But IoT devices as well as cloud and edges nodes must be AI-ready and be able to support the use of intelligent algorithms.

IET Quantum Communication•Sep 1, 2021•13 citations

Quantum neural network empowered spectral efficiency analysis in IRS-assisted communication systems

Md Habibur Rahman, Md Abdul Aziz, Rana Tabassum +3 more

The rapid increase in data consumption by mobile users has created a demand for innovative architectures to address this growing need. Concurrently, classical neural network-based intelligent reflecting surfaces (IRS) have gained traction in 6G networks due to their capacity to enhance spectral efficiency (SE). However, relying solely on classical computing for SE calculations imposes limitations on computational rates, stemming from the exponential increase in complexity. In response to this challenge, this paper proposes the use of quantum convolutional neural networks (QCNNs) to improve the SE of communication systems facilitated by IRS configurations. To optimize IRS phase shifts, we first apply singular value decomposition to the received IRS-optimized data. The resulting information is then incorporated into the quantum data and subsequently processed by the parameterized quantum circuit. A dense layer is introduced after the proposed model to boost training performance. The proposed QCNN model demonstrates a significant enhancement in SE while maintaining lower complexity, leveraging the capabilities of qubits in the current era of quantum computing.

Alexandria Engineering Journal•Aug 29, 2025•1 citation

Quantum Machine Learning for Channel Estimation in Reconfigurable Intelligent Surfaces-Assisted 6G Networks

Nhien Q. T. Thoong, Quang Nhat Le, Minh-Hien T. Nguyen +5 more

Sixth-generation (6G) wireless networks are expected to deliver transformative capabilities including ultra-reliable low-latency communications and massive connectivity. Reconfigurable intelligent surfaces (RISs) have emerged as a key enabling technology by dynamically manipulating wireless propagation through programmable reflecting elements. However, accurate channel estimation in RIS-assisted networks faces significant challenges from cascaded channel structures, passive elements, and high-dimensional parameter spaces. Quantum machine learning (QML) offers a fundamentally different computational paradigm that may address these challenges through quantum properties. This article provides a tutorial overview of QML for channel estimation in RIS-assisted 6G networks. We present RIS-assisted system architectures and channel estimation challenges, introduce quantum computing fundamentals, and survey QML architectures. As a use case, we consider a hybrid quantum-classical framework, e.g., convolutional neural network (CNN)-quantum long short-term memory (QLSTM), for channel estimation in RIS-assisted non-orthogonal multiple access (NOMA) systems. This case study shows consistent accuracy improvements compared to quantum and classical benchmarks. Finally, we discuss implementation challenges and future research directions for practical deployment of quantum-enhanced wireless systems.

IEEE Communications Standards Magazine•Jan 1, 2026•1 citation

Intelligent Quantum Computing Communication Interfaces for Secure and Scalable Distributed Quantum Systems

Bojjam Tejasri, Busa Praneeth, Dr T Anvesh Dr T Anvesh

Quantum computing communication interfaces are emerging as a fundamental technology for enabling seamless interaction between quantum processors, classical computing systems, cloud infrastructures, and distributed quantum networks. The development of efficient communication interfaces is essential to address challenges such as quantum state transmission, synchronization, error correction, latency, scalability, and interoperability across heterogeneous computing environments. This paper presents a comprehensive study of quantum computing communication interfaces by examining their architecture, communication protocols, hardware components, and software integration mechanisms. The proposed framework incorporates quantum-classical hybrid communication, quantum networking principles, secure quantum key distribution, adaptive resource management, and intelligent error mitigation techniques to improve communication reliability and computational efficiency. Furthermore, the study investigates the role of emerging technologies such as quantum internet, photonic interconnects, edge-cloud integration, and artificial intelligence in enhancing interface performance. Performance evaluation demonstrates that the proposed communication interface architecture achieves reduced communication latency, improved transmission fidelity, enhanced scalability, optimized resource utilization, and stronger security compared with conventional quantum communication approaches. The findings indicate that intelligent quantum communication interfaces can significantly accelerate the practical deployment of distributed quantum computing systems and future large-scale quantum applications.

International Journal of Science Strategic Management and Technology•Jul 27, 2026•0 citations

Quantum-Based Maximum Likelihood Detection in MIMO-NOMA Systems for 6G Networks

Helen Urgelles, David García‐Roger, José F. Monserrat

As wireless networks advance toward the Sixth Generation (6G), which will support highly heterogeneous scenarios and massive data traffic, conventional computing methods may struggle to meet the immense processing demands in a resource-efficient manner. This paper explores the potential of quantum computing (QC) to address these challenges, specifically by enhancing the efficiency of Maximum-Likelihood detection in Multiple-Input Multiple-Output (MIMO) Non-Orthogonal Multiple Access (NOMA) communication systems, an essential technology anticipated for 6G. The study proposes the use of the Quantum Approximate Optimization Algorithm (QAOA), a variational quantum algorithm known for providing quantum advantages in certain combinatorial optimization problems. While current quantum systems are not yet capable of managing millions of physical qubits or performing high-fidelity, long gate sequences, the results indicate that QAOA is a promising QC approach for radio signal processing tasks. This research provides valuable insights into the potential transformative impact of QC on future wireless networks. This sets the stage for discussions on practical implementation challenges, such as constrained problem sizes and sensitivity to noise, and opens pathways for future research aimed at fully harnessing the potential of QC for 6G and beyond.

Quantum Reports•Oct 22, 2024•10 citations
To achieve the goal of a seamless-connected world with communication assistance, quantum technology is crucial. However, the key limitations are the limited availability of network resources and regulated radio frequency spectrum for future commitments. Future technology will enable billions of connected devices to communicate smoothly, manage data effectively, and achieve computational accuracy. Designing an intelligently operated system with dynamic networks can only offer expanded network capabilities, which is necessary. Quantum computing is a fascinating use of quantum physics with broad implications. Quantum computing, which processes information using quantum bits (qubits), has fundamentally altered how humans process and send information. This study analyses the integration of an Intelligent Reflecting Surface (IRS) with quantum computing with a hybrid Nonorthogonal Multiple Access (NOMA) technique. It shows an increased sum rate of 28 bps/Hz (an improvement of 33%) and a minimal outage probability. To understand the properties of quantum states, Wigner function plots are analysed for three cat states: even, odd, and compass. Thus, the suggested framework is suitable for the upcoming next-generation wireless technologies.
Intelligent reflecting surfaces (IRSs) have attracted attention as a technology that can considerably improve the energy utilization efficiency of sixth-generation (6G) mobile communication systems. IRSs enable control of propagation characteristics by adjusting the phase shift of each reflective element. However, designing the phase shift requires the acquisition of channel information for each reflective element, which is impractical from an overhead perspective. In addition, for multiple wireless network operators to share an IRS for communication, new infrastructure facilities and operational costs are required at each operator's end to control the IRS in a coordinated manner. Herein, we propose a wireless communication system using standalone IRSs to solve these problems. The standalone IRSs cover a wide area by periodically switching phase shifts, and each operator allocates radio resources according to their phase-shift switching. Furthermore, we derive a quadratic unconstrained binary optimization equation for the proposed system to optimize radio resource allocation using quantum computing. The results of computer simulations indicate that the proposed system and method can be used to achieve efficient communication in 6G mobile communication systems.
While the commercial deployment of 5G networks and beyond is a reality, the first work for the definition and study of 6G networks has started. The scientific community agrees that these networks will be characterised by a very high spatial density of access points, heterogeneity of access technologies, an increased number of users per access point, and demand for ubiquitous connectivity that must combine ultra-low latency, very high bandwidth, and high energy efficiency [1, 2]. Among the emerging challenges, holographic communications, high-precision manufacturing, the ubiquitous introduction of intelligence, and the incorporation of new technologies based on sub-terahertz (THz) or Visible Light Communications (VLC) are real issues. These are taking place in a truly three-dimensional coverage framework, integrating terrestrial and aerial radio to meet the needs with cloud-based capabilities where and when needed (on-demand). Radio frequencies are used in wireless telecommunications, but the need for very high throughput requires wider bandwidths, hence very high frequencies, in particular THz bands. Moving to a higher frequency range—from 100 GHz to 10 THz—is expected to significantly increase the bandwidth of the radio channel, which will make it possible to serve a significant number of users. In this case, we are not talking about the connection of cell phones, tablets, or computers (and even smart cars)—we are considering the use of Internet of Things (IoT) devices, which within one base station can be quite a lot. Therefore, the technologies for beamforming, device location, etc., developed for the 5G generation that is just being implemented now should also remain but will be used at higher frequencies [3]. For which 6G is primarily intended, IoT solutions have been given a particular name: ‘human-machine-things’. They involve three elements in the system: a person as a physical carrier; an intelligent device with which the person interacts; collects data and executes commands from an application running on the person's device [4]. The 6G radio networks will provide the means of communication and data gathering necessary to accumulate information. Still, a system's approach will be required for the 6G technology market as a whole involving data analytics, artificial intelligence (AI), and next-generation computation capabilities via HPC and quantum computing [3, 5]. This tremendous amount of data may be harnessed, with strong processing and learning capabilities, to manage the network at different levels. To this end, quantum computing methods can play a significant enabling role and can provide a guaranteed security platform. Towards provisioning this massive connectivity and efficiently processing the voluminous data available at the user and network sides, quantum-powered computing methods have a strong potential in realising the ambitions of a service-driven, fully intelligent 6G communication network. There is every reason to believe that the integration of quantum technology can improve the throughput, efficiency, and security of 6G networks. In addition to the advantages mentioned above, this technology also has benefits in computing speed, guaranteed security, and minimal storage requirements. This makes it ideally suited for various future quantum communication network applications. Research is needed on many fronts to evaluate the potential opportunities of quantum computing in 6G applications. In 5G, a functional division is operated in both the RAN and core networks to meet the ultra-low latency and ultra-high efficiency requirements of 5G verticals. Notably, RAN functions are split into cloud-deployed services with interfacing capabilities. This new architecture has a significant impact on the efficient implementation of security processes [6]. One of the key challenges in deploying secure 5G networks is analysing how current cryptographic algorithms for secure authentication and data transmission can adapt to provide secure authentication between services and secure data encryption while ensuring that stringent efficiency requirements are met [7]. The powerful parallel processing capability of quantum search algorithms can be leveraged to solve large-scale wireless optimisation problems. (1) Is this ‘beyond 5G?’ This topic is highly exploratory and undoubtedly beyond the boundaries of core 5G R&D. (2) What are the new features and key challenges? Classically, the mutual information between the channel input and output must be maximised. For quantum channels, the capacity must be redefined. Quantum channels can increase the achievable capacity. As a result, error correction without redundancy is possible on noisy channels. Quantum computers can enable exponentially complex combinatorial ML decoding in classical systems [5, 6]. To cope with latency-critical scenarios such as automated cars and medical applications, communication must be accomplished in a concise time. Achieving latency of only a few milliseconds is a real challenge. To achieve low latency and ultra-high reliability, it is essential to develop powerful high-end processors that consume little power. Quantum computing seems to be a reliable solution to this issue [8]. With variable quality of service (QoS) requirements, a variable radio resource must be allocated to the user. This can be of variable bandwidth, variable power, or both. Another challenging aspect of 6G is that with the propagation frequency, the signal can attenuate quickly and have a high penetration loss at high frequencies [9]. The signal is also automatically attenuated when accessing homes, residences, or workplaces. As the frequency increases, radio waves attenuate and may have difficulty penetrating the walls of buildings and homes while affecting users' QoS requirements. Therefore, it is very important to develop stable, fast and accurate quantum algorithms to handle 6G communication requirements by dynamically allocating variable resources [10]. The current 5G standard does not address the issue of quantum computing but relies on traditional cryptography. The development towards cloud and edge native infrastructures is expected to continue in 6G networks. While large-scale quantum computing can be expected to take a longer duration, it is time to prepare for the shift to cryptography that is secure in the post-quantum world. According to current knowledge, contemporary symmetric cryptography remains secure for the most part, even after the advent of quantum computing. Furthermore, authentication by a physical layer signature, such as RF fingerprinting, and some other technologies, such as randomisation of MIMO transmission coefficients, coding, etc., could potentially be used in 6G [5, 11]. Whether it is for the optimisation of heterogeneous resources in 6G networks that result in the solution of ‘NP-complete’ combinatorial problems, for the compensation of non-linear effects of a communication chain or the optimisation of MIMO transmitter pre-coding matrix, future network nodes and devices will have to be equipped with learning and decision-making capabilities through AI and machine learning, so that they autonomously optimise their operation in response to changes in the environment [5]. In this context, the use of optimisation techniques based on quantum computing and AI applied to the field of 6G networks could be of considerable interest. Besides, quantum computing allows NP-hard optimisation problems to be solved in linear time, making it possible to solve and execute network optimisation problems in a much faster time, even in real-time [5, 11]. Regulatory and standardisation work will impact our future technology solutions in 5G networks and beyond. Protecting individual privacy and identity has long been a challenge for standards bodies, as countries have very different perspectives and regulations in this area. International standards bodies such as JTC 1 have committees working on privacy frameworks and are making progress, but there is still much to be done [13]. To conclude, we believe that our readers will find this opinion piece useful in giving an overview of the importance of Quantum Computing/communications in shaping the next communication systems and its security platforms. This is a technology in its infancy and a lot of research remains to be carried out to achieve sustainable solutions and overcome the current challenges. The use of intelligent systems (AI, ML and other techniques) will be essential in achieving solutions faster and providing sustainable outcomes. But IoT devices as well as cloud and edges nodes must be AI-ready and be able to support the use of intelligent algorithms.
The rapid increase in data consumption by mobile users has created a demand for innovative architectures to address this growing need. Concurrently, classical neural network-based intelligent reflecting surfaces (IRS) have gained traction in 6G networks due to their capacity to enhance spectral efficiency (SE). However, relying solely on classical computing for SE calculations imposes limitations on computational rates, stemming from the exponential increase in complexity. In response to this challenge, this paper proposes the use of quantum convolutional neural networks (QCNNs) to improve the SE of communication systems facilitated by IRS configurations. To optimize IRS phase shifts, we first apply singular value decomposition to the received IRS-optimized data. The resulting information is then incorporated into the quantum data and subsequently processed by the parameterized quantum circuit. A dense layer is introduced after the proposed model to boost training performance. The proposed QCNN model demonstrates a significant enhancement in SE while maintaining lower complexity, leveraging the capabilities of qubits in the current era of quantum computing.
Sixth-generation (6G) wireless networks are expected to deliver transformative capabilities including ultra-reliable low-latency communications and massive connectivity. Reconfigurable intelligent surfaces (RISs) have emerged as a key enabling technology by dynamically manipulating wireless propagation through programmable reflecting elements. However, accurate channel estimation in RIS-assisted networks faces significant challenges from cascaded channel structures, passive elements, and high-dimensional parameter spaces. Quantum machine learning (QML) offers a fundamentally different computational paradigm that may address these challenges through quantum properties. This article provides a tutorial overview of QML for channel estimation in RIS-assisted 6G networks. We present RIS-assisted system architectures and channel estimation challenges, introduce quantum computing fundamentals, and survey QML architectures. As a use case, we consider a hybrid quantum-classical framework, e.g., convolutional neural network (CNN)-quantum long short-term memory (QLSTM), for channel estimation in RIS-assisted non-orthogonal multiple access (NOMA) systems. This case study shows consistent accuracy improvements compared to quantum and classical benchmarks. Finally, we discuss implementation challenges and future research directions for practical deployment of quantum-enhanced wireless systems.
Quantum computing communication interfaces are emerging as a fundamental technology for enabling seamless interaction between quantum processors, classical computing systems, cloud infrastructures, and distributed quantum networks. The development of efficient communication interfaces is essential to address challenges such as quantum state transmission, synchronization, error correction, latency, scalability, and interoperability across heterogeneous computing environments. This paper presents a comprehensive study of quantum computing communication interfaces by examining their architecture, communication protocols, hardware components, and software integration mechanisms. The proposed framework incorporates quantum-classical hybrid communication, quantum networking principles, secure quantum key distribution, adaptive resource management, and intelligent error mitigation techniques to improve communication reliability and computational efficiency. Furthermore, the study investigates the role of emerging technologies such as quantum internet, photonic interconnects, edge-cloud integration, and artificial intelligence in enhancing interface performance. Performance evaluation demonstrates that the proposed communication interface architecture achieves reduced communication latency, improved transmission fidelity, enhanced scalability, optimized resource utilization, and stronger security compared with conventional quantum communication approaches. The findings indicate that intelligent quantum communication interfaces can significantly accelerate the practical deployment of distributed quantum computing systems and future large-scale quantum applications.
As wireless networks advance toward the Sixth Generation (6G), which will support highly heterogeneous scenarios and massive data traffic, conventional computing methods may struggle to meet the immense processing demands in a resource-efficient manner. This paper explores the potential of quantum computing (QC) to address these challenges, specifically by enhancing the efficiency of Maximum-Likelihood detection in Multiple-Input Multiple-Output (MIMO) Non-Orthogonal Multiple Access (NOMA) communication systems, an essential technology anticipated for 6G. The study proposes the use of the Quantum Approximate Optimization Algorithm (QAOA), a variational quantum algorithm known for providing quantum advantages in certain combinatorial optimization problems. While current quantum systems are not yet capable of managing millions of physical qubits or performing high-fidelity, long gate sequences, the results indicate that QAOA is a promising QC approach for radio signal processing tasks. This research provides valuable insights into the potential transformative impact of QC on future wireless networks. This sets the stage for discussions on practical implementation challenges, such as constrained problem sizes and sensitivity to noise, and opens pathways for future research aimed at fully harnessing the potential of QC for 6G and beyond.