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.
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.
Physical-layer key generation (PLKG) is an information-theoretic security technique aimed at addressing key distribution challenges among resource-constrained legitimate users in the Internet of Things (IoT). However, in quasi-static or blocked environments, key generation faces significant limitations owing to its low entropy. To overcome this challenge, we propose leveraging a simultaneously transmitting and reflecting (STAR) reconfigurable intelligent surface (RIS). In this study, we introduce a STAR-RIS-aided multiuser PLKG framework and formulate an optimization problem focused on minimizing the average bit disagreement ratio through optimal phase-shift matrix adjustments. Initially, we derive closed-form solutions for optimal phase shifts, simplifying the problem to depend solely on amplitude coefficients. We then employ a Newton-Raphson algorithm under the energy-splitting (ES) protocol and a traversal method under the mode-switching (MS) and time-switching (TS) protocols to solve the optimization problem. Simulation results confirm the efficacy of our proposed schemes by comparison with existing benchmark schemes.
This paper investigates the simultaneously transmitting and reflecting reconfigurable surface (STAR-RIS) assisted physical layer key generation (PKG) for multiuser networks. We propose a novel STAR-RIS-assisted PKG protocol and formulate a sum secret key rate maximization problem by jointly optimizing the active and passive beamforming. To address the non-convex problem, a block coordinate descent (BCD) algorithm is developed, using both semidefinite relaxation (SDR) and successive convex approximation (SCA) methods to optimize active and passive precoding alternately. Simulation results demonstrate the key generation performance of the proposed algorithm outperforms schemes that STAR-RISs using the random configuration and conventional reflecting/transmitting-only RISs.
A robust full-space physical layer security (PLS) transmission scheme is proposed in this paper considering the full-space wiretapping challenge of wireless networks supported by simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). Different from the existing schemes, the proposed PLS scheme takes account of the uncertainty on the eavesdropper’s position within the 360◦ service area offered by the STAR-RIS. Specifically, the large system analytical method is utilized to derive the asymptotic expression of the average security rate achieved by the security user, considering that the base station (BS) only has the statistical information of the eavesdropper’s channel state information (CSI) and the uncertainty of its location. To evaluate the effectiveness of the proposed PLS scheme, we first formulate an optimization problem aimed at maximizing the weighted sum rate of the security user and the public user. This optimization is conducted under the power allocation constraint, and some practical limitations for STAR-RIS implementation, through jointly designing the active and passive beamforming variables. A novel iterative algorithm based on the minimum mean-square error (MMSE) and cross-entropy optimization (CEO) methods is proposed to effectively address the established non-convex optimization problem with discrete variables. Simulation results indicate that the proposed robust PLS scheme can effectively mitigate the information leakage across the entire coverage area of the STAR-RIS-assisted system, leading to superior performance gain when compared to benchmark schemes encompassing traditional RIS-aided scheme.
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 , 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.
By exploiting the entropy of wireless channels, physical layer key generation (PLKG) has gained considerable attention in recent years. Due to the characteristic of dynamic controlling of wireless channels, reconfigurable intelligent surface (RIS) can improve the key generation rate (KGR). Simultaneous transmission and reflection reconfigurable intelligent surface (STAR- RIS) can extend the half-space coverage to full-space coverage. This paper proposes a STAR-RIS-assisted physical layer key generation (SRKG) scheme in a multi-user case. The objective is to maximize the sum KGR by jointly designing the transmitting and reflecting coefficients (TARCs). We propose an iterative algorithm based on alternating optimization (AO), utilizing successive convex approximation (SCA) and semi-definite relaxation (SDR) methods, to solve the non-convex optimization problem in the presence of correlated channels. We also propose a low-complexity algorithm based on Lagrange multipliers to jointly optimize TARCs considering independent fading. Finally, the simulation results validate that, compared with the PLKG scheme assisted by traditional RIS (T-RIS), the proposed SRKG scheme can achieve higher sum KGR in a wider range under correlated and independent channel conditions.