Inspired by recent developments in various areas of science relevant to quantum computing, we introduce quantum manifold optimization (QMO) as a promising framework for solving constrained optimization problems in next-generation wireless communication systems. We begin by showing how classical wireless design problems - such as pilot design in cell-free (CF)-massive MIMO (mMIMO), beamformer optimization in gigantic multiple input multiple output (MIMO), and reconfigurable intelligent surface (RIS) phase tuning - naturally reside on structured manifolds like the Stiefel, Grassmannian, and oblique manifolds, with the latter novelly formulated in this work. Then, we demonstrate how these problems can be reformulated as trace-based quantum expectation values over variationally-encoded quantum states. While theoretical in scope, the work lays a foundation for a new class of quantum optimization algorithms with broad application to the design of future beyond-sixth-generation (B6G) systems.
Inspired by recent developments in various areas of science relevant to quantum computing, we introduce quantum manifold optimization (QMO) as a promising framework for solving constrained optimization problems in next-generation wireless communication systems. We begin by showing how classical wireless design problems - such as pilot design in cell-free (CF)-massive MIMO (mMIMO), beamformer optimization in gigantic multiple input multiple output (MIMO), and reconfigurable intelligent surface (RIS) phase tuning - naturally reside on structured manifolds like the Stiefel, Grassmannian, and oblique manifolds, with the latter novelly formulated in this work. Then, we demonstrate how these problems can be reformulated as trace-based quantum expectation values over variationally-encoded quantum states. While theoretical in scope, the work lays a foundation for a new class of quantum optimization algorithms with broad application to the design of future beyond-sixth-generation (B6G) systems.
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.
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.
Networks built to sixth-generation (6G) targets will have to hold sub-millisecond latency, push spectral efficiency past what today's massive MIMO deployments achieve, and manage resources across highly heterogeneous nodes with little human supervision. Those targets place a hard demand on the optimizer. Channel estimation, adaptive modulation, beamforming and decoding all involve searching large, non-convex parameter spaces, and the search has to finish inside a coherence interval that may last only a few hundred microseconds. This paper surveys quantum-inspired signal processing (QISP), a family of classical algorithms that borrow structure from quantum computation, including amplitude-weighted superposition sampling, quantum-walk-style exploration, Ising-model formulations and tensor-network compression, but that run entirely on conventional processors. We group the literature under four headings: algorithmic foundations, communication-system architectures, optimization strategies, and implementation constraints. We then propose a reference architecture in which a quantum-inspired optimization engine sits beside a conventional OFDM/MIMO transmit-receive chain and reconfigures modulation, beam-selection and decoding parameters from channel-state feedback. An illustrative simulation grounds the discussion: a representative QISP optimizer is compared against a conventional adaptive baseline over a 4×4 MIMO, QPSK, AWGN-channel OFDM link at signal-to-noise ratios (SNR) from 0 to 20 dB. These numbers demonstrate methodology and are not measurements taken from physical hardware. Within that caveat, the QISP optimizer achieves a lower biterror rate (BER) at low and moderate SNR, and the throughput and latency trends are consistent with the parameter-search efficiency reported in the wider literature. The paper concludes with open problems such as scalability, algorithmic complexity, explainability and standardized benchmarking, and research directions on hybrid quantum-classical processors, reconfigurable intelligent surfaces (RIS), semantic communications, and edge-native QISP inference.
Quantum optimization is poised to play a transformative role in the design of next-generation wireless communication systems by addressing key computational and technological challenges. This article provides an overview of the principles of adiabatic quantum computing, the foundation of quantum optimization, and explores its two primary computational models: quantum annealing and the gate-based quantum approximate optimization algorithm. By highlighting their core features, performance benefits, limitations, and distinctions, we position these methods as promising tools for advancing wireless communication system design. As a case study, we examine the design of passive reconfigurable intelligent surface beamforming with binary phase-shift resolution, supported by experimental results obtained from real-world quantum hardware.
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.
The proposal outlines the design of a radar and relay-assisted communication system with multiple input multiple output (MIMO) configuration, utilizing quantum algorithm in coexistence scenario. We name it MIMO QRadRCom framework. Quantum computation offers significant speed-ups and holds promise for future technologies like sixth generation (6G) cellular networks. By integrating quantum simulation, we aim to design parameters, like an equalizer at the communication receiver and others, and then classically optimize the rest of the design parameters. The equalizer comes from the linear minimum mean square error (LMMSE) criterion, which involves matrix inversion and is classically expensive. The mathematical formulations address classical and quantum inaccuracies, supported by numerical simulations on the IBM quantum machine.