Quantum computing (QC)-assisted algorithms promise exponential increase in the computational efficiency, enabling instant solution of large systems of equations. Massive multiple-input multiple-output (MIMO) millimeter-wave (mmWave) systems pose extraordinary demands in computational complexity, due to the usage of huge antenna arrays, massive number of users, and ultra low-latency requirements. This work provides an initial discussion on how QC-based algorithms for solving linear systems of equations could be utilized for assisting the basic operations of the transceivers physical-layer, such as the channel estimation. We identify the connections between the amplitude encoding in the quantum domain and the recovery of the channel information.
Quantum computing (QC)-assisted algorithms promise exponential increase in the computational efficiency, enabling instant solution of large systems of equations. Massive multiple-input multiple-output (MIMO) millimeter-wave (mmWave) systems pose extraordinary demands in computational complexity, due to the usage of huge antenna arrays, massive number of users, and ultra low-latency requirements. This work provides an initial discussion on how QC-based algorithms for solving linear systems of equations could be utilized for assisting the basic operations of the transceivers physical-layer, such as the channel estimation. We identify the connections between the amplitude encoding in the quantum domain and the recovery of the channel information.
Abstract The paper explores the field of quantum-enhanced channel estimation of terahertz (THz) cell-free massive multiple-input multiple-output (MIMO) systems. Although quantum sensing has been suggested to be used in wireless communications, its practical benefit over classical techniques is not well known. We identify systematically the signal-to-noise ratio (SNR) regime in which quantum sensing is able to offer an advantage. The results demonstrate that THz systems operating in the low pilot SNR regime ( ∼ ∼ 3 dB) achieve 14–103.1% sum-rate improvement with quantum-enhanced estimation, compablack to only 2–5% at millimeter-wave (mmWave) frequencies where pilot SNR exceeds 13 dB. The 6–33 × × greater quantum advantage at THz frequencies stems from the fundamental difference in operating regimes: THz systems are noise-limited due to severe path loss ( ∼ ∼ 100 dB at 10 m), while mmWave systems are interference-limited with near-perfect classical estimation. Our results identify THz cell-free multiple-input multiple-output (MIMO) as a compelling application for quantum-enhanced wireless communications and provide design guidelines for practical deployment in future sixth-generation (6G) networks.
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.
Massive multiple-input multiple-output (MIMO) systems are a key enabling technology for 5G-Advanced and emerging 6G networks, yet their performance is fundamentally constrained by high-dimensional, non-convex optimization problems arising from beamforming, power allocation, and user scheduling under imperfect channel state information (CSI).This paper proposes a hybrid quantum-classical optimization framework that formulates discrete and continuous massive MIMO resource allocation problems using Quadratic Unconstrained Binary Optimization (QUBO) and variational quantum algorithms.Discrete beam selection is addressed using quantum annealing, while continuous power control and hybrid beamforming are refined using variational quantum circuits within a classical feedback loop.Simulation results demonstrate improvements in aggregate spectral efficiency, runtime scalability, and robustness under imperfect CSI compared to conventional ZF and WMMSE baselines.The benefits of using the proposed solution include scalable runtime performance with respect to larger numbers of antennas; thus, it highlights the potential of optimizing quantum-assisted technologies for the future of wireless networks based on realistic assumptions regarding near-term NISQ-era performance.
A linear system can be solved more efficiently by quantum computing. However, previously known quantum algorithms provide only a quantum state as the solution; consequently, we cannot obtain the value of each component of the solution. We propose a method to extract the component values of the solution, and we present an application to linear multiple-input multiple-output (MIMO) detections. In the proposed algorithm, we demonstrate a concrete method that applies a quantum linear system algorithm (QLSA) when the components of a solution have binary variables, quaternary variables, or roots of a complex number. Whereas the conventional method requires an additional process to read out the values of the components, the proposed algorithm does not need any post-procedure. Instead, our method uses a QLSA iteratively, and the number of uses is logarithmic in the size of the linear system. Thus, our method maintains the runtime with the quantum advantage, but the conventional approach increases the runtime significantly. Furthermore, the application of the proposed method shows that quantum computing can collaborate with communication systems for large-scale MIMO systems.
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.
Massive multiple-input multiple-output (MIMO) has gained widespread popularity in recent years due to its ability to increase data rates, improve signal quality, and provide better coverage in challenging environments. In this paper, we investigate the MIMO beam selection (MBS) problem, which is proven to be NP-hard and computationally intractable. To deal with this problem, quantum computing that can provide faster and more efficient solutions to large-scale combinatorial optimization is considered. MBS is formulated in a quadratic unbounded binary optimization form and solved with Coherent Ising Machine (CIM) physical machine. We compare the performance of our solution with two classic heuristics, simulated annealing and Tabu search. The results demonstrate an average performance improvement by a factor of 261.23 and 20.6, respectively, which shows that CIM-based solution performs significantly better in terms of selecting the optimal subset of beams. This work shows great promise for practical 5G operation and promotes the application of quantum computing in solving computationally hard problems in communication.