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IEEE Open Journal of Vehicular Technology

Phase-Aware Quantum–Classical Feedback Compression in RIS: Trainability and Performance Limits

Sudhanshu Arya

Published Jan 1, 2026• 0 citations
Publisher

Abstract

This paper considers a multi-antenna base station (BS) communicating with a single-antenna user through a reconfigurable intelligent surface (RIS). In large RIS deployments, feeding back the high-dimensional phase vector $\boldsymbol{\theta }\in [0,2\pi)^{N}$ , where $N$ is the number of RIS elements, poses a major scalability challenge. Conventional codebook-based compression is limited to fixed linear subspaces. To address this, we propose Phase-Aware Quantum RIS (PAQ-RIS), a hybrid quantum–classical autoencoder that compresses RIS phase profiles using variational quantum circuits. Phase shifts are angle-encoded into quantum states, processed by parameterized quantum circuits, and reconstructed by a classical decoder. Results show that PAQ-RIS improves beamforming accuracy by up to 14.5% over classical autoencoders for an RIS with $N=512$ elements using $q=9$ qubits at a feedback compression ratio of approximately 19%. For $N=1024$ , the framework continues to provide performance gains, achieving beamforming accuracy improvements of up to 11.33%. However, for larger RIS configurations with $N=2048$ elements, the performance gain decreases, with the best improvement dropping to 4.44% due to barren plateaus and near-Haar-random latent states, establishing the practical scalability limits of quantum-assisted compression.

DOI
10.1109/ojvt.2026.3721274

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This paper considers a multi-antenna base station (BS) communicating with a single-antenna user through a reconfigurable intelligent surface (RIS). In large RIS deployments, feeding back the high-dimensional phase vector θ∈[0,2π)N\boldsymbol{\theta }\in [0,2\pi)^{N}θ∈[0,2π)N , where NNN is the number of RIS elements, poses a major scalability challenge. Conventional codebook-based compression is limited to fixed linear subspaces. To address this, we propose Phase-Aware Quantum RIS (PAQ-RIS), a hybrid quantum–classical autoencoder that compresses RIS phase profiles using variational quantum circuits. Phase shifts are angle-encoded into quantum states, processed by parameterized quantum circuits, and reconstructed by a classical decoder. Results show that PAQ-RIS improves beamforming accuracy by up to 14.5% over classical autoencoders for an RIS with N=512N=512N=512 elements using q=9q=9q=9 qubits at a feedback compression ratio of approximately 19%. For N=1024N=1024N=1024 , the framework continues to provide performance gains, achieving beamforming accuracy improvements of up to 11.33%. However, for larger RIS configurations with N=2048N=2048N=2048 elements, the performance gain decreases, with the best improvement dropping to 4.44% due to barren plateaus and near-Haar-random latent states, establishing the practical scalability limits of quantum-assisted compression.
Reconfigurable intelligent surface (RIS) optimization is a high-dimensional combinatorial problem, where classical methods face significant scalability challenges. The quantum approximate optimization algorithm (QAOA) offers a promising alternative by encoding the optimization problem into a quantum Hamiltonian and finding its ground state through a parameterized quantum circuit, known as the ansatz [1]. In the quantum framework, the RIS optimization problem is mapped onto the Sherrington-Kirkpatrick (SK) Hamiltonian, which models the transfer function between the transmitting antenna (TX) and the receiver (RX) [2], [3] as follows:
Quantum-classical hybrid communication is emerging as a key enabler for future 6G networks, where THz communication and quantum services are expected to coexist over intelligent and reconfigurable wireless infrastructures. A fundamental challenge in such systems is that reconfigurable intelligent surface (RIS) phase control simultaneously influences classical communication performance and quantum-state evolution, making reliable entanglement preservation difficult in dynamic propagation environments. To address this challenge, this paper proposes an AI-native Entanglement-Sustained Reconfigurable Intelligent Surface (AI-ES-RIS) framework for hybrid quantum-classical THz communication. A unified model is developed to jointly characterize THz-wave propagation and Bell-state evolution through a phase-flip-inspired decoherence representation. To capture RIS-induced quantum degradation, a weighted mismatch-based decoherence model is introduced that links RIS phase perturbations, channel-dependent element significance, and entanglement fidelity degradation. Furthermore, a Proximal Policy Optimization (PPO)-based controller is designed to adapt RIS phase configurations by jointly optimizing communication reliability and quantum-state preservation. Simulation results demonstrate that the proposed framework consistently achieves higher entanglement fidelity, lower decoherence probability, and competitive communication performance compared with communication-centric and model-based RIS optimization strategies. Distributional analysis further confirms improved quantum-state robustness across diverse channel realizations, while scalability studies demonstrate favorable runtime characteristics for larger RIS dimensions. Additional meta-learning and federated learning extensions show enhanced adaptation and convergence under heterogeneous propagation environments. The results establish that RIS control in hybrid quantum-classical networks should be treated as a joint communication-and-quantum optimization problem rather than as independent design objectives, providing a promising foundation for future THz-enabled quantum-aware 6G systems.
In this paper, we investigate the potential of a near-optimal hybrid quantum-classical optimization approach to jointly optimizing beamforming and discrete phase shifts of the simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) assisted wireless network. We first formulate a discrete optimization problem to maximize the total power transmitted to the ground users by optimizing the beamforming at the base station (BS) and STAR-RIS phase shift cells under minimal power allocation for each user and the power budget at the BS. Then, we propose a quantum approximate optimization algorithm with alternating optimization (QAOA-AO) method that iteratively addresses beamform-ing components and discrete phase shifts to search for the near-optimal solutions for the problem. Numerical results validate the effectiveness and robustness of the proposed QAOA-AO compared to the classical benchmarks, and further highlight its potential for practical applicability for solving medium-to-large-scale optimization problems.
Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS) enables 360-degree full-space coverage by controlling both transmitted and reflected signals through each element. However, the coupled phase-shift model, where the transmission and reflection coefficients are physically interdependent, transforms the phase optimization into an extremely complex non-convex combinatorial problem that classical algorithms struggle to solve efficiently. In this paper, we propose a hybrid quantum-classical framework that employs the Quantum Approximate Optimization Algorithm (QAOA) to optimize the coupled phase-shift configuration of STAR-RIS. Each discrete phase configuration is mapped to a qubit-based Ising Hamiltonian, enabling QAOA to explore the exponentially large solution space through quantum superposition and entanglement. To address the scalability challenge of simulating large antenna arrays (e.g., 8 × 8 = 64 elements), we introduce a variable grouping (tiling) strategy that partitions the RIS into manageable sub-arrays of 2 × 2 elements, each requiring only 8 qubits for 2-bit phase quantization. Our comprehensive simulations demonstrate that the proposed QAOA-based optimizer achieves an approximation ratio exceeding 0.94 with 10 QAOA layers, maintains over 86% fidelity under 2% gate error rates in noisy intermediate-scale quantum (NISQ) environments, and exhibits robust performance under imperfect channel state information with less than 5% degradation at 30% CSI error variance. The energy efficiency analysis confirms that the QAOA-optimized STAR-RIS achieves approximately 1.7× higher spectral efficiency per unit energy compared to conventional reflect-only RIS. Furthermore, the proposed tiling framework scales linearly with array size, enabling deployment on arrays up to 16 × 16 elements while maintaining constant per-group quantum resource requirements.
Wireless communication systems play a pivotal role in modern society, yet they face significant challenges such as latency and multipath fading. Reconfigurable intelligent surface (RIS) is emerged as a promising solution to manipulate electromagnetic waves to enhance transmission quality, although their optimization presents several limitations. In this study, we propose a hybrid approach utilizing the quantum approximate optimization algorithm (QAOA) to effectively configure RIS in multipath environments, addressing the shortcomings of classical methods. The computational model trained by the Sherrington- Kirkpatrick Hamiltonian, demonstrates high accuracy in identifying optimal RIS configurations across various scenarios, without running optimization at each condition. However, the analysis of barren plateaus reveals that the cost function gradient diminishes exponentially as the number of cells increases, making hard the training for large-scale systems. To mitigate these issues, we conclude by suggesting some potential strategies for future research aimed at enhancing RIS performance in practical applications.
Quantum networks (QNs) supported by terahertz (THz) wireless links present a transformative alternative to fiber-based infrastructures, particularly in mobile and infrastructure-scarce environments. However, signal attenuation, molecular absorption, and severe propagation losses in THz channels pose significant challenges to reliable quantum state transmission and entanglement distribution. To overcome these limitations, we propose a dynamic reconfigurable intelligent surface (RIS)-assisted wireless QN architecture that leverages adaptive RIS elements capable of switching between active and passive modes based on the incident signal-to-noise ratio (SNR). These dynamic RIS elements enhance beamforming control over amplitude and phase, enabling robust redirection and compensation for THz-specific impairments through real-time measurement-driven adjustments. We develop a detailed analytical model that incorporates key physical layer phenomena in THz quantum links, including path loss, fading, thermal noise, and alignment variations. This model also acts as a digital twin, simulating quantum channel measurements under uncertainty. A secure optimization framework is formulated to jointly determine RIS placement and entanglement generation rate (EGR) allocation, while satisfying fidelity, security, and fairness constraints under diverse quality of service (QoS) demands [1] . Fidelity and leakage metrics are treated as measurable quantities, reinforcing the role of measurement in system adaptation. Simulation results demonstrate that the proposed architecture yields up to 87% fidelity enhancement and 65% fairness improvement compared to static RIS baselines, while maintaining robustness under realistic THz channel conditions. The integration of adaptive RIS control and fidelity-aware modeling highlights the digitalization of quantum measurement processes. These results underscore the promise of dynamic RIS technology in enabling scalable and adaptive quantum communications over wireless THz links.