The design and analysis of reconfigurable meta-surfaces operating within rich multi-path propagation fading is of crucial importance for the development of real-life programmable electromagnetic environments. We incorporate the effect of multi-path fading in an impedance-based model of wireless communication links assisted by reconfigurable surfaces. Previous work has shown that impedance-based channel models under rich multi-path propagation have an isomorphism with Sherrington-Kirkpatrick (SK) Hamiltonians. We focus on received power minimisation, which is equivalent to the hard-to-solve task task of finding the ground state of the SK Hamiltonian. It has been recently discovered that the Quantum Approximate Optimisation Algorithm (QAOA) predicts the ground state of SK Hamiltonians accurately. However, the landscape parameters of QAOA are dependent on the specific realisation of the random SK Hamiltonian, which hinders the full usage of quantum hardware to optimise reconfigurable surfaces dynamically. We show by Montecarlo simulations that a concentration property cures this impediment thus making QAOA an excellent candidate for surface optimisation under fast multi-path fading.
The design and analysis of reconfigurable meta-surfaces operating within rich multi-path propagation fading is of crucial importance for the development of real-life programmable electromagnetic environments. We incorporate the effect of multi-path fading in an impedance-based model of wireless communication links assisted by reconfigurable surfaces. Previous work has shown that impedance-based channel models under rich multi-path propagation have an isomorphism with Sherrington-Kirkpatrick (SK) Hamiltonians. We focus on received power minimisation, which is equivalent to the hard-to-solve task task of finding the ground state of the SK Hamiltonian. It has been recently discovered that the Quantum Approximate Optimisation Algorithm (QAOA) predicts the ground state of SK Hamiltonians accurately. However, the landscape parameters of QAOA are dependent on the specific realisation of the random SK Hamiltonian, which hinders the full usage of quantum hardware to optimise reconfigurable surfaces dynamically. We show by Montecarlo simulations that a concentration property cures this impediment thus making QAOA an excellent candidate for surface optimisation under fast multi-path fading.
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:
Wireless communication technology has become important in modern life. Real-world radio environments present significant challenges, particularly concerning latency and multipath fading. A promising solution is represented by reconfigurable intelligent surfaces (RIS), which can manipulate electromagnetic waves to enhance transmission quality. In this study, we introduce a novel approach that employs the quantum approximate optimization algorithm (QAOA) to efficiently configure RIS in multipath environments. Applying the spin glass (SG) theoretical framework to describe chaotic systems, along with a variable noise model, we propose a quantum-based minimization algorithm to optimize RIS in various electromagnetic scenarios affected by multipath fading. The method involves training a parameterized quantum circuit using a mathematical model that scales with the size of the RIS. When applied to different EM scenarios, it directly identifies the optimal RIS configuration. This approach eliminates the need for large datasets for training, validation, and testing, streamlines, and accelerates the training process. Furthermore, the algorithm will not need to be rerun for each individual scenario. In particular, our analysis considers a system with one transmitting antenna, multiple receiving antennas, and varying noise levels. The results show that QAOA enhances the performance of RIS in both noise-free and noisy environments, highlighting the potential of quantum computing to address the complexities of RIS optimization and improve the performance of the wireless network.
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.
Reconfigurable intelligent surfaces (RIS) maximize received power by setting per-element phases. Discrete-phase optimization is NP-hard in the worst case, while the quantum approximate optimization algorithm (QAOA) applied to RIS faces limited phase alphabets, either per-problem angle optimization or uncharacterized training cost exposed to barren plateaus, and no scalable performance benchmark. We introduce a 2M-phase θ dictionary for optimizing power ∥Aejθ∥2 having K×N channel matrix A and QAOA angle offline optimization with instance and size-independent infinite-size limit of the mixed-q Gaussian ensemble of Basso et al. Our design bounds the spin-Hamiltonian interaction order to at most quartic for any M, and the deployed order-2 reduction lies below the even-q≥4 regime in which constant-level QAOA limitations are proved. We perform analytical, state-vector, matrix-product-state and Pauli-path-simulation numerical studies for N=K≤100 and QAOA depth p=9, verifying offline angle transfer to Rayleigh, Rician/line-of-sight, cascaded double-fading and spatially-correlated RIS channels at N∈{5,12}. We observe performance reaching a near-optimal multi-start single-flip local-search reference for N≤16 under order-2 modeling with 25=32-phase dictionary while the order-4 model shows a performance ceiling below the classical reference. The approach suggests a route to near-optimal large-N performance on future fault-tolerant (FTQ) quantum computers, which enable the higher-depth QAOA circuits.
Recently, there has been a growing interest in employing reconfigurable intelligent surfaces (RISs) to improve the spectrum and energy efficiency of wireless networks. In RIS-assisted wireless networks, channel estimation and optimization is a difficult task, particularly for nearly passive RIS devices with low-complexity hardware design. We have proposed a hybrid classical-quantum computing framework that allows for ultra-fast optimization adapting to multipath wireless environments. The onsite optimization of RIS configuration can be performed almost instantaneously using only feedback (received power) at wireless endpoints. The performance of the proposed work is demonstrated in representative wireless propagation scenarios.
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.