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

IEEE Open Journal of the Communications Society

When are Quantum Algorithms Applicable for Signal Decoding in Wireless Communication?

Abdulmohsen A. Alsaui, Ibrahim Al-Nahhal, Octavia A. Dobre

Published Jan 1, 2024• 6 citations
Publisher

Abstract

Multiple-input multiple-output (MIMO) technology utilizes multiple antennas at the transmitter and receiver to enhance data transmission speeds and reliability. Traditional MIMO decoding methods, however, can become increasingly complex as the number of antennas and modulation order rises. Quantum computing brings forth a new realm of information processing with significant potential. This study investigates the applicability of quantum algorithms for decoding information in MIMO wireless communication systems. Specifically, the Dürr-Høyer quantum search, based on Grover’s algorithm, and optimal quantum sorting algorithms are leveraged to reduce the query complexity with an analysis of the complexity and achievable bit error rate performance of the quantum-assisted decoders. The study considers cases of MIMO diversity, spatial modulation, and multiplexing transmission using both maximum-likelihood and fixed-complexity sphere decoders. By examining a variety of communication scenarios, this work aims to assess the applicability of quantum algorithms across different operational regimes and aids in extending the methodology to other communication systems.

DOI
10.1109/ojcoms.2024.3461334

Related papers

Quantum Partial Sorting for Efficient Signal Decoding in Spatially Modulated Wireless Systems

Abdulmohsen A. Alsaui, Ibrahim Al-Nahhal, Octavia A. Dobre

This paper introduces a novel quantum-assisted algorithm for partial sorting, which is integrated with a decoding algorithm for spatially modulated wireless signals. The decoder achieves near-optimal bit error rate performance, closely matching that of the maximum-likelihood decoder, while significantly reducing computational complexity. Additionally, integrating the quantum partial sorting algorithm lowers the query complexity. Although quantum algorithms introduce a small failure probability, resulting in an error floor, simulations validate the scalability and practical efficiency of the utilized decoder with quantumassisted sorting.

IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)•Jul 7, 2025•1 citation

Hybrid Quantum-Classical Approaches to Optimize Signal Processing in Massive MIMO Arrays

Shaik Anjimoon, Baswaraju Swathi, Rajeev Sobti +3 more

This paper introduces a pioneering method in the realm of signal processing for Massive Multiple-Input Multiple-Output (MIMO) systems, leveraging the synergistic potential of hybrid quantum-classical computing paradigms. The escalating demand for high data rate communications necessitates the advancement of MIMO technologies. However, the computational complexity associated with signal processing in such systems, especially in scenarios involving large array sizes, presents a significant challenge. This study proposes an innovative framework that integrates quantum computing algorithms with traditional classical methods to address this challenge. The quantum algorithms are specifically designed to expedite matrix operations and optimization tasks that are central to MIMO signal processing, such as channel estimation and precoding. A comparative analysis with purely classical approaches demonstrates a substantial improvement in computational efficiency and latency reduction, while maintaining, and in certain aspects enhancing, the accuracy of signal detection and system throughput. Furthermore, this hybrid approach offers scalability and adaptability to varying channel conditions and system configurations, a crucial feature for next-generation wireless communication systems. The theoretical foundation is complemented by simulation results, showcasing the feasibility and effectiveness of this approach in practical scenarios. This research not only paves the way for advanced signal processing techniques in Massive MIMO systems but also opens new horizons in the application of quantum computing in telecommunications.

International Conference on Communication, Computer Sciences and Engineering (IC3SE)•May 9, 2024•4 citations

A quantum search based signal detection for MIMO-OFDM systems

Fei Li, Lizhi Zhou, Li Liu +1 more

Multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) is considered as candidates for future broadband wireless services. In this paper a novel signal detection scheme based on Grover's quantum search algorithm is proposed for MIMO-OFDM systems. Grover's quantum search algorithm is based on the concept and principles of quantum computing, such as quantum bit, quantum register and quantum parallelism. An analysis is given to the theoretical basis of Grover's algorithm and the performance of Grover's algorithm is evaluated. A novel signal detector based on Grover's algorithm (GD) for MIMO-OFDM system is proposed. The simulation results show that the proposed detector has more powerful properties in bit error rate than MMSE detector and VBLAST-MMSE detector. The performance of the proposed GD detector is close to optimal when the failure probability is 0.001. When the failure probability is 0.00001, the performance of GD detector declines. In this case, our proposed improved Grover's algorithm based detector is still close to the optimal ML detector. The complexity of GD and IGD is O(√N). It's much better than classical ML detector which complexity is O(N).

•May 1, 2011•4 citations

Quantum Partial Sorting for Signal Decoding in Wireless Communication Systems

Abdulmohsen A. Alsaui, Ibrahim Al-Nahhal, Octavia A. Dobre +1 more

This work proposes a novel quantum-assisted partial sorting algorithm, called multi-minima Dürr–Høyer (MMDH), designed to reduce query complexity in scenarios where only a small subset of elements must be sorted. Empirical results show that MMDH significantly outperforms classical algorithms in these settings, achieving over an order of magnitude reduction in query complexity. The algorithm is applied to signal detection in multiple-input multiple-output systems and is particularly effective when integrated into a newly introduced variable-complexity sphere decoder, called progressive tree expansion (PTE), which inherently benefits from partial sorting. Compared to fixed-complexity sphere decoders (FCSDs), the PTE algorithm substantially reduces the computational complexity, especially at high signal-to-noise ratios (SNRs). When augmented with MMDH, the quantum-assisted PTE decoder achieves near maximum-likelihood error performance while mitigating the query overhead commonly associated with tree-based decoders. In contrast, conventional FCSDs benefit less from MMDH, as they require selection of multiple minima rather than partial ordering, a task where classical methods such as heap-based selection remain competitive. Although MMDH introduces a small failure probability, the resulting error floor stays below practical thresholds in high-SNR regimes.

IEEE Journal on Selected Areas in Communications•Jan 1, 2026•0 citations

Integration of Quantum Computing Techniques in MIMO System Optimization

Csl Vijaya Durga, R J Anandhi, Bashetty Suman +3 more

The rapid advancement of quantum computing provides unprecedented opportunities for the optimization of multiple-input, multiple-output (MIMO) systems. This paper investigates the efficacy of harnessing quantum algorithms, particularly the Quantum Approximate Optimization Algorithm (QAOA) and Grover's algorithm, for the optimization of MIMO systems. The paper establishes a theoretical framework that maps MIMO optimization problems onto quantum circuits, ensuring a reduction in computational complexity compared to classical algorithms. The proposed methodology is validated through a series of numerical simulations. Notable improvements are observed in both convergence speed and the quality of the optimized solutions. Furthermore, the synergy of quantum computing and MIMO presents new frontiers for reducing interference, enhancing throughput, and ensuring robustness against environmental uncertainties. These results affirm the potential of integrating quantum computing techniques in MIMO system optimization, heralding a transformative era for wireless communication paradigms.

International Conference on Power Energy, Environment and Intelligent Control (PEEIC)•Dec 19, 2023•5 citations

Grover's Quantum Search for the Signal Detection of OFDMA Downlink and SC-FDMA Uplink

Swaraj Mondal, Nazmun Nahar Zarin, Mohammad Ismat Kadir

There are different access systems proposed for next-generation wireless communications. As the number of users in wireless systems increases, the challenge of reliable communications can be faced with the aid of exhaustive search-based signal detection. However, exhaustive maximum-likelihood searches impose a high complexity. In classical communication systems, performance improvement is achieved at the cost of substantially increased computational complexity. In this study, we consider an OFDMA downlink and SC-FDMA uplink scenario. We propose the employment of quantum search methods for signal detection. Specifically, we employ Grover's algorithm for database search in signal detection. We observe from our simulations that the error performance provided by the classical ML search is attainable by our method. As a benefit of using quantum search, the complexity imposed is substantially reduced compared to using classical ML detectors. We also compare the performance of the proposed scheme with Grover's search-based MC-CDMA detector. We investigate with multiple users as well and find that the quantum search-based scheme is effective in improving performance, while the computational burden is very low.

International Conference on Electrical Engineering and Information Communication Technology•May 2, 2024•1 citation
Multiple-input multiple-output (MIMO) technology utilizes multiple antennas at the transmitter and receiver to enhance data transmission speeds and reliability. Traditional MIMO decoding methods, however, can become increasingly complex as the number of antennas and modulation order rises. Quantum computing brings forth a new realm of information processing with significant potential. This study investigates the applicability of quantum algorithms for decoding information in MIMO wireless communication systems. Specifically, the Dürr-Høyer quantum search, based on Grover’s algorithm, and optimal quantum sorting algorithms are leveraged to reduce the query complexity with an analysis of the complexity and achievable bit error rate performance of the quantum-assisted decoders. The study considers cases of MIMO diversity, spatial modulation, and multiplexing transmission using both maximum-likelihood and fixed-complexity sphere decoders. By examining a variety of communication scenarios, this work aims to assess the applicability of quantum algorithms across different operational regimes and aids in extending the methodology to other communication systems.
This paper introduces a novel quantum-assisted algorithm for partial sorting, which is integrated with a decoding algorithm for spatially modulated wireless signals. The decoder achieves near-optimal bit error rate performance, closely matching that of the maximum-likelihood decoder, while significantly reducing computational complexity. Additionally, integrating the quantum partial sorting algorithm lowers the query complexity. Although quantum algorithms introduce a small failure probability, resulting in an error floor, simulations validate the scalability and practical efficiency of the utilized decoder with quantumassisted sorting.
This paper introduces a pioneering method in the realm of signal processing for Massive Multiple-Input Multiple-Output (MIMO) systems, leveraging the synergistic potential of hybrid quantum-classical computing paradigms. The escalating demand for high data rate communications necessitates the advancement of MIMO technologies. However, the computational complexity associated with signal processing in such systems, especially in scenarios involving large array sizes, presents a significant challenge. This study proposes an innovative framework that integrates quantum computing algorithms with traditional classical methods to address this challenge. The quantum algorithms are specifically designed to expedite matrix operations and optimization tasks that are central to MIMO signal processing, such as channel estimation and precoding. A comparative analysis with purely classical approaches demonstrates a substantial improvement in computational efficiency and latency reduction, while maintaining, and in certain aspects enhancing, the accuracy of signal detection and system throughput. Furthermore, this hybrid approach offers scalability and adaptability to varying channel conditions and system configurations, a crucial feature for next-generation wireless communication systems. The theoretical foundation is complemented by simulation results, showcasing the feasibility and effectiveness of this approach in practical scenarios. This research not only paves the way for advanced signal processing techniques in Massive MIMO systems but also opens new horizons in the application of quantum computing in telecommunications.
Multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) is considered as candidates for future broadband wireless services. In this paper a novel signal detection scheme based on Grover's quantum search algorithm is proposed for MIMO-OFDM systems. Grover's quantum search algorithm is based on the concept and principles of quantum computing, such as quantum bit, quantum register and quantum parallelism. An analysis is given to the theoretical basis of Grover's algorithm and the performance of Grover's algorithm is evaluated. A novel signal detector based on Grover's algorithm (GD) for MIMO-OFDM system is proposed. The simulation results show that the proposed detector has more powerful properties in bit error rate than MMSE detector and VBLAST-MMSE detector. The performance of the proposed GD detector is close to optimal when the failure probability is 0.001. When the failure probability is 0.00001, the performance of GD detector declines. In this case, our proposed improved Grover's algorithm based detector is still close to the optimal ML detector. The complexity of GD and IGD is O(√N). It's much better than classical ML detector which complexity is O(N).
This work proposes a novel quantum-assisted partial sorting algorithm, called multi-minima Dürr–Høyer (MMDH), designed to reduce query complexity in scenarios where only a small subset of elements must be sorted. Empirical results show that MMDH significantly outperforms classical algorithms in these settings, achieving over an order of magnitude reduction in query complexity. The algorithm is applied to signal detection in multiple-input multiple-output systems and is particularly effective when integrated into a newly introduced variable-complexity sphere decoder, called progressive tree expansion (PTE), which inherently benefits from partial sorting. Compared to fixed-complexity sphere decoders (FCSDs), the PTE algorithm substantially reduces the computational complexity, especially at high signal-to-noise ratios (SNRs). When augmented with MMDH, the quantum-assisted PTE decoder achieves near maximum-likelihood error performance while mitigating the query overhead commonly associated with tree-based decoders. In contrast, conventional FCSDs benefit less from MMDH, as they require selection of multiple minima rather than partial ordering, a task where classical methods such as heap-based selection remain competitive. Although MMDH introduces a small failure probability, the resulting error floor stays below practical thresholds in high-SNR regimes.
The rapid advancement of quantum computing provides unprecedented opportunities for the optimization of multiple-input, multiple-output (MIMO) systems. This paper investigates the efficacy of harnessing quantum algorithms, particularly the Quantum Approximate Optimization Algorithm (QAOA) and Grover's algorithm, for the optimization of MIMO systems. The paper establishes a theoretical framework that maps MIMO optimization problems onto quantum circuits, ensuring a reduction in computational complexity compared to classical algorithms. The proposed methodology is validated through a series of numerical simulations. Notable improvements are observed in both convergence speed and the quality of the optimized solutions. Furthermore, the synergy of quantum computing and MIMO presents new frontiers for reducing interference, enhancing throughput, and ensuring robustness against environmental uncertainties. These results affirm the potential of integrating quantum computing techniques in MIMO system optimization, heralding a transformative era for wireless communication paradigms.
There are different access systems proposed for next-generation wireless communications. As the number of users in wireless systems increases, the challenge of reliable communications can be faced with the aid of exhaustive search-based signal detection. However, exhaustive maximum-likelihood searches impose a high complexity. In classical communication systems, performance improvement is achieved at the cost of substantially increased computational complexity. In this study, we consider an OFDMA downlink and SC-FDMA uplink scenario. We propose the employment of quantum search methods for signal detection. Specifically, we employ Grover's algorithm for database search in signal detection. We observe from our simulations that the error performance provided by the classical ML search is attainable by our method. As a benefit of using quantum search, the complexity imposed is substantially reduced compared to using classical ML detectors. We also compare the performance of the proposed scheme with Grover's search-based MC-CDMA detector. We investigate with multiple users as well and find that the quantum search-based scheme is effective in improving performance, while the computational burden is very low.