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Journal of Infrared Millimeter and Terahertz Waves

Quantum-Enhanced Channel Estimation for Terahertz Cell-Free Massive MIMO: Identifying the Favorable SNR Regime

Aqiel Niama Almamori, Mohammed Adil Abbas, Hasan Kahtan, Imran Baig

Published Jul 1, 2026• 0 citations
Publisher

Abstract

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 ( $$\sim $$ ∼ 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 $$\times $$ × greater quantum advantage at THz frequencies stems from the fundamental difference in operating regimes: THz systems are noise-limited due to severe path loss ( $$\sim $$ ∼ 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.

DOI
10.1007/s10762-026-01156-x

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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 ( ∼\sim ∼ ∼ 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 ×\times × × greater quantum advantage at THz frequencies stems from the fundamental difference in operating regimes: THz systems are noise-limited due to severe path loss ( ∼\sim ∼ ∼ 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.
We propose a multiple-input multiple-output (MIMO) quantum key distribution (QKD) scheme for terahertz (THz) frequency applications operating at room temperature. Motivated by classical MIMO communications, a transmit-receive beamforming scheme is proposed that converts the rank-rrr MIMO channel between Alice and Bob into rrr parallel lossy quantum channels. Compared with existing single-antenna QKD schemes, we demonstrate that the MIMO QKD scheme leads to performance improvements by increasing the secret key rate and extending the transmission distance. Our simulation results show that multiple antennas are necessary to overcome the high free-space path loss at THz frequencies. We demonstrate a non-monotonic relation between performance and frequency, and reveal that positive key rates are achievable in the 10–30 THz frequency range. The proposed scheme can be used for both indoor and outdoor QKD applications for beyond fifth generation ultra-secure wireless communications systems.
We propose a multiple-input multiple-output (MIMO) quantum key distribution (QKD) scheme for terahertz (THz) frequency applications operating at room temperature. Motivated by classical MIMO communications, a transmit-receive beamforming scheme is proposed that converts the rank-rrrMIMO channel between Alice and Bob intorrrparallel lossy quantum channels. Compared with existing single-antenna QKD schemes, we demonstrate that the MIMO QKD scheme leads to performance improvements by increasing the secret key rate and extending the transmission distance. Our simulation results show that multiple antennas are necessary to overcome the high free-space path loss at THz frequencies. We demonstrate a non-monotonic relation between performance and frequency, and reveal that positive key rates are achievable in the 10–30 THz frequency range. The proposed scheme can be used for both indoor and outdoor QKD applications for beyond fifth generation ultra-secure wireless communications systems.
With the emergence of sixth-generation (6G) wireless networks, ensuring information-theoretic security at extremely high data rates has become a critical challenge. Conventional cryptographic techniques are increasingly vulnerable to quantum computing attacks, motivating the integration of Quantum Key Distribution (QKD) with advanced wireless technologies. This paper proposes a hybrid QKD–Massive MIMO framework operating in the terahertz (THz) band (10–30 THz) to evaluate secure key generation performance under realistic distance, frequency, and antenna scaling conditions. A security-aware Secret Key Rate (SKR) model is developed by incorporating Quantum Bit Error Rate (QBER) constraints and distance-dependent attenuation. Extensive simulations analyze SKR variation with transmission distance (1–10 m), carrier frequency, and antenna array size (32–1024). Results demonstrate that massive MIMO significantly enhances SKR and extends the secure communication range, while higher THz frequencies introduce stronger attenuation. The proposed framework validates the feasibility of scalable quantum-secure THz communications for future 6G networks.
We study the secret key rate (SKR) of a multiple-input multiple-output (MIMO) continuous variable quantum key distribution (CVQKD) system operating at terahertz (THz) frequencies, accounting for the effects of channel estimation. We propose a practical channel estimation scheme for the THz MIMO CVQKD system which is necessary to realize transmit-receive beamforming between Alice and Bob. We characterize the input-output relation between Alice and Bob during the key generation phase, by incorporating the effects of additional noise terms arising due to the channel estimation error and detector noise. Furthermore, we analyze the SKR of the system and study the effect of channel estimation error and overhead. Our simulation results reveal that the SKR may degrade significantly as compared to the SKR upper bound that assumes perfect channel state information, particularly at large transmission distances.
Although multiple-input multiple-output (MIMO) terahertz (THz) continuous-variable quantum key distribution (CVQKD) is theoretically secure, practical vulnerabilities may arise due to detector imperfections. This paper explores a CV measurement-device-independent (MDI) QKD system operating at THz frequencies within a MIMO framework. In this system, measurement is delegated to an untrusted third party, Charlie, rather than the receiver, eliminating all detector attacks and significantly enhancing the system's practical security. Using transmit-receive beamforming techniques, the system transforms MIMO channels into multiple parallel lossy quantum channels, enabling robust key distribution between Alice and Bob. This study examines entanglement-based and prepare-and-measure protocols, deriving secret key rates for both asymptotic and finite code scenarios. Simulations reveal the critical role of multiple antenna configurations and efficient homodyne detection in mitigating free-space path loss and maximizing key rates. Results indicate that system performance is optimized at lower THz frequencies for long-range transmissions and higher frequencies for short-range applications. The proposed protocol offers a scalable solution for secure quantum communications in next-generation wireless networks, demonstrating potential for deployment in both indoor and outdoor environments.
Channel estimation is one of the most fundamental and performance-limiting operations in fifth-generation (5G) and emerging sixth-generation (6G) wireless networks, where ultra-massive MIMO arrays, millimetre-wave and terahertz carrier frequencies, and non-Gaussian hardware-induced noise combine to render classical least-squares and minimum mean squared error estimators inadequate. Deep learning has narrowed the performance gap, yet it offers no information-theoretic guarantee on how close any particular network architecture is to the fundamental noise floor. This paper introduces the AI-Quantum Channel Modeling and Estimation (AQCME) framework, which integrates variational quantum circuits, quantum signal processing, and a quantum eigenspace denoising operator into a unified trainable architecture for simultaneous channel estimation and noise suppression in MIMO-OFDM systems. The defining theoretical result is a quantum Cramer-Rao lower bound on normalised mean squared error, grounded in the quantum Fisher information matrix of the encoded channel state, together with a rigorous proof that the AQCME estimator approaches this bound as training data grows – to the best of our knowledge the first constructive achievability result of its kind in the wireless channel estimation literature. The framework is supported by formal analysis of quantum channel state preparation, quantum kernel universality with an explicit barren-plateau trainability discussion, ergodic spectral efficiency, Nakagami-m outage probability, and convergence of the hybrid quantum-classical gradient descent. Monte Carlo simulations over 50,000 channel realisations, reported with 90 percent confidence intervals throughout, validate every theoretical prediction. Ablation studies across circuit depths and qubit counts, a hardware-noise sweep over gate error rates from 1e-4 to 1e-2, and hyperparameter sensitivity analysis confirm robustness. At 20 dB signal-to-noise ratio, AQCME reduces normalised mean squared error by 4.8 dB over MMSE and by 2.1 dB over the best deep learning baseline; under emulated near-term noisy quantum hardware conditions it still outperforms MMSE by 2.6 dB. All quantum results in this work are obtained by exact classical statevector emulation of the variational circuit; no physical quantum processor was used, and reported hardware-noise behaviour is modelled by a depolarising channel. Spectral efficiency improves by up to 22.4 percent in massive MIMO scenarios, the URLLC outage target of 1e-5 is met at 6.2 dB lower signal-to-noise ratio than with MMSE, and inference complexity scales logarithmically with antenna count rather than quartically as with MMSE matrix inversion.