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International Conference on Mobile Networks and Wireless Communications (ICMNWC)

Adaptive Quantum Signal Processing Framework for Secure and Reliable Wireless Communication

R. Palanivel, J. Harirajkumar, Ch. Rajendra Prasad, G. Keerthana, M Vinay

Published Dec 10, 2025• 0 citations
Publisher

Abstract

In recent years, quantum technologies have developed as transformative enablers for upcoming generation secure wireless communication by incorporating quantum sensing, computation, and cryptographic techniques for secure reliable data transmission. However, traditional quantum receiver models like Rydberg Atomic Quantum Receivers (RAQR), remain controlled by static signal processing, unmodeled noise interference, and the absence of incorporated quantum-level security. Hence, this research proposes a Secure Quantum Signal Processing Receiver (SQSP-R) which is an adaptive and noise-resilient framework that combines Quantum Signal Processing (QSP) with phase-based quantum encryption. Initially experimental data is gathered using simulated wireless transmission scenarios under changing frequency bands ($\mathbf{1 0} \boldsymbol{-} \mathbf{4 5}$ GHz), laser detuning levels, and noise settings to estimate system robustness and accuracy. This method applies a reconfigurable SU (2) rotation-based QSP layer that permits dynamic filtering, interference suppression, and phase-controlled demodulation. After that, a quantum random key generator confirms intrinsic encryption during the signal transformation process. Furthermore, a quantum reliability model quantitatively estimates decoherence, optical noise, and environmental disturbances over a real-time feedback optimization mechanism. This incorporated method confirms that the consistent signal fidelity and improved operational adaptability. Experimental evaluation established that the proposed SQSP-R highly enhance the reconstruction performance, attaining a Root Mean Square Error (RMSE) of $\mathbf{0.45}-\mathbf{0.95}$ compared to the existing RAQR model.

DOI
10.1109/icmnwc66779.2025.11354314

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Physical layer security (PLS) provides information-theoretic confidentiality for next-generation wireless systems, yet its practical deployment is fundamentally constrained by the sensitivity and noise characteristics of classical radio receivers. Recent advances in quantum sensing, particularly Rydberg atom-based receivers (RAQRs), present a promising approach to address these limitations. This paper introduces a Rydberg atomic quantum multiple-input multiple-output (RAQ-MIMO) architecture for secure multi-user uplink communications, where an array of Rydberg vapor cells functions as a high-sensitivity, reconfigurable receiving front-end. We develop a comprehensive end-to-end signal and channel model that incorporates quantum sensor response, wireless fading, and eavesdropper behavior under passive interception. To maximize the achievable secrecy rate, we propose a joint optimization framework that adaptively tunes key system degrees of freedom, including the Rabi frequency of the local oscillator, phase configuration, digital combining matrix, and user transmit power. Extensive simulations validate that RAQ-MIMO substantially outperforms classical MIMO in terms of secrecy rate across varying transmission distances, power budgets, and user loads—particularly under low-signal to noise ratio (SNR) and extended-range regimes. Our work establishes a quantum-sensing-assisted security foundation for future high-assurance wireless networks, bridging atomic physics with scalable communication system design with scalable communication system design under narrowband operation.
The significant progress of quantum sensing technologies offer numerous radical solutions for measuring a multitude of physical quantities at an unprecedented precision. Among them, Rydberg atomic quantum receivers (RAQRs) emerge as an eminent solution for detecting the electric field of radio frequency (RF) signals, exhibiting great potential in assisting classical wireless communications and sensing. So far, most experimental studies have aimed for the proof of physical concepts to reveal its promise, while the practical signal model of RAQR-aided wireless communications and sensing remained under-explored. Furthermore, the performance of RAQR-based wireless receivers and their advantages over classical RF receivers have not been fully characterized. To fill these gaps, we introduce the RAQR to the wireless community by presenting an end-to-end reception scheme. We then develop a corresponding equivalent baseband signal model relying on a realistic reception flow. Our scheme and model provide explicit design guidance to RAQR-aided wireless systems. We next study the performance of RAQR-aided wireless systems based on our model, and compare them to classical RF receivers. The results show that Doppler broadening-free RAQRs are capable of achieving a substantial received signal-to-noise ratio (SNR) gain of over 27 decibel (dB) and 40 dB in the photon shot limit and standard quantum limit regimes, respectively.
Quantum Signal Processing (QSP) offers a paradigm shift in signal analysis, leveraging the unique properties of quantum mechanics to achieve unprecedented precision and resilience. This paper investigates the development of a novel adaptive robust QSP algorithm designed to withstand noise and perturbations while dynamically adjusting processing strategies to accommodate environmental changes. The core mechanism centers on the integration of quantum error correction and the dynamic interplay between quantum states, allowing the system to maintain accurate signal extraction even under challenging conditions. We present a detailed analysis of the algorithm's design, demonstrating its effectiveness through theoretical analysis and preliminary experimental validation. The research highlights the significant advantages of this approach compared to traditional methods, emphasizing its potential to revolutionize a broad spectrum of signal processing applications.
Abstract: Quantum signal processing (QSP) provides a mathematically economical mechanism for transforming the spectral information of a block-encoded operator through carefully phased single-qubit rotations. Photonic quantum computing, meanwhile, offers high-bandwidth operation, network compatibility and substantial room-temperature functionality, yet remains constrained by optical loss, probabilistic state preparation, finite squeezing, detector imperfections and the resource overhead of logical error correction. This paper develops an integrated analytical framework that connects QSP polynomial synthesis to fault-tolerant photonic execution and to a broader construct termed quantum-enhanced scientific intelligence: the reliable extraction of predictive, explanatory and decision-relevant knowledge from complex scientific operators and data. The study adopts a theory-building and model-based research design. It combines block encoding, QSP and quantum singular-value transformation with open-system dynamics, logical-error modelling, constrained multi-objective optimization and risk-adjusted outcome scoring. The proposed QSP-FTSI framework treats algorithmic approximation error, photonic component quality, decoder performance, logical reliability, resource intensity, uncertainty calibration and scientific utility as coupled rather than independent variables. An illustrative normalized model yields a baseline benefit index of 7.942, a composite risk index of 2.640 and a risk-adjusted score of 6.358 on a ten-point scale; an improved low-loss, high-QEC scenario reaches 7.858, whereas a stressed configuration falls to 3.754. These values are demonstrations, not experimental findings. The principal contribution is a publication-ready mathematical architecture for evaluating when QSP-enabled photonic systems can support scientifically meaningful simulation, inverse modelling, spectral filtering and inference under explicit fault-tolerance and reproducibility constraints. The framework also identifies measurable pathways for experimental validation, hardware–algorithm co-design and responsible scientific deployment. Keywords quantum signal processing; quantum singular-value transformation; fault-tolerant photonic computing; block encoding; Gottesman–Kitaev–Preskill codes; fusion-based quantum computation; logical error suppression; Hamiltonian simulation; scientific machine learning; quantum-enhanced inference; photonic integrated circuits; spectral transformation; quantum error correction; risk-adjusted scientific intelligence; hardware–algorithm co-design
Making sense of the world happens through receiving and processing signals, whether it be sound, light, or other waves that interact with us. The fields of nuclear magnetic resonance and digital signal processing each built a rigorous language for manipulating and extracting information from signals of particular types. Quantum signal processing (QSP) connects these two traditions, providing a unified algebraic framework for understanding how quantum systems are transformed by both classical and quantum signals. QSP provides a systematic framework for characterizing possible transformations. QSP was developed for qubit systems, but physical quantum systems are rarely two-level and the full richness of high-dimensional Hilbert spaces has remained largely unexplored. This thesis extends and applies QSP in two directions: from qubits to high-dimensional systems such as finite-level qudits and infinite-dimensional oscillators and from static signals to signals that include continuous-time noise processes. We will also show how QSP for qudits leads to novel experimental applications in trapped-ion systems. One type of high-dimensional system is a qubit coupled to an oscillator, and a qubit-oscillator qudit can be formed and universally controlled using only a limited set of interactions. In this qubit-oscillator space, a bosonic version of QSP allows novel incarnations of quantum algorithms and sensing. The quantum Fourier transform can be performed by clever conversion of information in a many-qubit system into a single oscillator, and an interferometric protocol uses binary decisions from a qubit to sense a signal affecting the oscillator. Another type of high-dimensional system is a qudit made from the electronic levels of an atom. This thesis presents a new construction of Grover’s algorithm using a qudit in an atomic hyperfine manifold, along with a calibration method that finds accurate QSP pulse parameters for the experiment. For decision algorithms, QSP in qudits enables theoretically perfect decision-making for quantum channel discrimination problems with certain symmetries. In the face of noise, temporal QSP is developed to extend the canonical framework to continuous-time stochastic noise. Combined with Kubo linear-response theory, a noise filter function description emerges which formalizes the optimization of atomic clock performance. These results from qudit, bosonic, and temporal QSP establish QSP as a natural language for high-dimensional quantum control and sensing.
Despite rapid advances in quantum hardware, noise remains a central obstacle to deploying quantum algorithms on near-term devices. In particular, random coherent errors that accumulate during circuit execution constitute a dominant and fundamentally challenging noise source. We introduce a noise-resilient framework for Quantum Signal Processing (QSP) that mitigates such coherent errors without increasing circuit depth or ancillary qubit requirements. Our approach uses ensembles of noisy QSP circuits combined with measurement-level averaging to suppress random phase errors in Z rotations. Building on this framework, we develop robust QSP algorithms for implementing polynomial functions of Hermitian matrices and for estimating observables, with applications to Hamiltonian simulation, quantum linear systems, and ground-state preparation. We analyze the trade-off between approximation error and hardware noise, which is essential for practical implementation under the stringent depth and coherence constraints of current quantum hardware. Our results establish a practical pathway for integrating error mitigation seamlessly into algorithmic design, advancing the development of robust quantum computing, and enabling the discovery of scientific applications with near- and mid-term quantum devices.