arXiv subjects

Neel Kanth Kundu

Publications and source records attributed to Neel Kanth Kundu.

At least 19 recordsLinked to original sources

arXiv2026

Quantum-Enhanced Classical Communications for DPMZM-based NG-PON2 Systems

This paper presents a framework for integrating quantum detection techniques into a Dual-Parallel Mach-Zehnder Modulator (DPMZM)-based Next Generation Passive Optical Network 2 (NG-PON2) communication system. We present a system model for the DPMZM-based NG-PON2 quantum communication system and characterize its signal-to-noise ratio (SNR), demonstrating its superiority over conventional MZM-based systems. We analyze the performance of various quantum modulation techniques, including binary phase-shift keying, quadrature phase-shift keying, and M-ary quadrature amplitude modulation. A comprehensive analytical framework is developed to evaluate the SNR, BER, and mutual information for both classical and quantum detection schemes within the DPMZM-based NG-PON2 quantum communication system. Our simulation results show that quantum detection significantly outperforms classical detection, and the DPMZM architecture further enhances the performance over the baseline MZM architecture by improving the effective modulation efficiency and receiver sensitivity.

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arXiv2026

A Tutorial on Practical QKD Post-Processing Algorithms Using a GUI-Based Simulation Framework

Recent experimental demonstrations of Quantum Key Distribution (QKD) have attracted significant attention for their ability to establish information-theoretically secure encryption keys for next-generation communication systems. As practical deployment accelerates, the realization of compact, scalable, and high-throughput discrete-variable (DV) QKD systems has become increasingly important. While advances in photonic integration are enabling low-size, low-weight, and low-power QKD hardware, the security and performance of these systems rely heavily on efficient post-processing techniques to generate identical, secure keys. This tutorial presents a comprehensive algorithm-level review of practical post-processing techniques for DV-QKD, with particular emphasis on information reconciliation methods based on Cascade, Winnow, low-density parity-check (LDPC), and polar codes. We also analyze the theoretical asymptotic and finite-size secret key rates (SKRs) of DV-QKD and benchmark these bounds against the practical SKRs achieved using actual post-processing algorithms. Furthermore, we present a server-client model to implement the entire QKD post-processing pipeline. The performance is evaluated in terms of QBER and SKR across various system parameters, considering the complete post-processing pipeline, including sifting, information reconciliation, privacy amplification, and authentication. This tutorial also presents a graphical user interface (GUI) for the QKD post-processing methods, enabling researchers, industry practitioners, and academic users to visualize and analyze the impact of different post-processing techniques and system parameters, such as QBER, on the SKR performance of DV-QKD systems.

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arXiv2026

Performance Analysis of Multiparty GHZ-State Quantum Conference Key Agreement over Optical Fiber Channel

Quantum conference key agreement (QCKA) enables multiple spatially separated users to establish a common key with composable information-theoretic security by exploiting multipartite quantum correlations. Greenberger--Horne--Zeilinger (GHZ) states are a natural resource for this task, but their distribution over optical fiber is degraded by transmission loss, polarization decoherence, and detector imperfections. We investigate the finite-key performance of an $N$-party GHZ-based QCKA system using the $N$-BB84 protocol, where each optical link is modeled by extending the bipartite erasure--Pauli channel to incorporate detector dark counts. We characterize the impact of this quantum channel on the shared GHZ state and derive closed-form expressions for the error rates. The analytical results reveal that the local bit-error rate depends only on transmissivity and dark counts, while the global phase-error rate compounds over all $N-1$ links and therefore degrades faster with both distance and the number of parties. These error rates are integrated into a finite-key secret-key-rate (SKR) framework, in which we optimize the parameter-estimation probability via Brent's derivative-free method. Numerical results show that increasing fiber distance and network size reduce the achievable SKR. Furthermore, the optimized parameter-estimation probability consistently increases the SKR and extends the maximum secure distance relative to a fixed-allocation baseline, with comparison to the asymptotic limit quantifying the residual finite-key penalty.

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arXiv2026

Efficient Quantum Algorithm for Phase Optimization of 1-Bit RIS-Assisted MIMO Communication System

We propose a Quantum Approximate Optimization Algorithm with a deterministic linear ramp schedule (QAOA-LR) for phase optimization of a 1-bit RIS-assisted MIMO communication system. Each RIS element is restricted to a binary phase shift of 0 or π, turning the passive beamforming design problem with N elements into a combinatorial optimization problem over 2^N configurations. Instead of running a classical optimizer, QAOA-LR uses a fixed linear ramp to set the variational parameters across p layers and finds the best scale via a simple one-dimensional grid search over a single parameter. Monte Carlo simulations over Rayleigh-fading MIMO channels confirm that QAOA-LR closely tracks the optimum maximum-likelihood (ML) solution. Furthermore, the proposed algorithm reduces the computational complexity compared with classical optimization, and real hardware experiments on the IBM Quantum processor confirm near-ML capacity performance with polynomial scaling of quantum processing unit execution time as the number of RIS elements increases.

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arXiv2026

Robust Quantum-MUSIC for DoA Estimation Using Rydberg Atomic Receiver Arrays

Quantum wireless sensing using Rydberg atomic receivers enables high-sensitivity signal acquisition direction-of-arrival (DoA) estimation. However, it suffers from a fundamental limitation, where only the magnitude of the received signal is observable. The recently proposed Quantum-MUSIC algorithm addresses this problem by recovering phase information through alternating minimization and subsequently applying the MUSIC algorithm for DoA estimation. However, the existing approach relies on an $\ell_2$-norm phase retrieval step, making it highly sensitive to outlier measurements produced by hardware faults, sensor saturation, or adversarial interference. In this letter, we propose a \emph{Robust Quantum-MUSIC} (RobQMUSIC) framework that replaces the $\ell_2$-norm with an $\ell_1$-norm formulation. The resulting weighted phase-retrieval problem is solved efficiently via an Iteratively Reweighted Least Squares (IRLS) scheme embedded within the alternating minimization loop, requiring no increase in structural complexity relative to the baseline algorithm. Simulation results demonstrate that RobQMUSIC achieves near-identical DoA estimation accuracy to Quantum-MUSIC under ideal conditions, while maintaining robust performance over a wide range of outlier contamination levels at which Quantum-MUSIC fails entirely.

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arXiv2026

Hearing the Ocean: Bio-inspired Gammatone-CNN framework for Robust Underwater Acoustic Target Classification

This study presents a bio inspired signal processing framework for robust Underwater Acoustic Target Recognition (UATR). The latest state of the art methods often fail to resolve dense low frequency harmonic structures in vessel propulsion signals under high noise conditions, which is addressed by the proposed framework using a biologically inspired Gammatone filter bank that emulates the cochlea nonlinear frequency selectivity. By distributing filters according to the Equivalent Rectangular Bandwidth (ERB) scale, the framework achieves a high fidelity representation of engine radiated tonals while effectively suppressing isotropic ambient interference. The resulting Cochleagram features are processed by a lightweight, custom designed Convolutional Neural Network (CNN) that leverages large receptive fields to integrate spectral-temporal continuities. Experimental results on the VTUAD dataset demonstrate a state of the art classification accuracy of 98.41%, outperforming Continuous Wavelet Transform and Mel Frequency Cepstral Coefficients baselines by 3.5% and 7.7% respectively. Furthermore, the framework achieves an inference latency of only 0.77 ms and a 0.971 Cohen Kappa score, validating its efficacy for real time deployment on autonomous, low-power sonar hardware.

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arXiv2026

Warm-Start Quantum Approximate Optimization Algorithm for QAM MIMO Data Detection

Data detection in large-scale multiple-input multiple-output (MIMO) systems with higher-order quadrature amplitude modulation (QAM) remains a challenging problem due to the exponential complexity of the classical maximum likelihood (ML) detector. This challenge is further amplified by Gray-coded modulation, which introduces nonlinear symbol-to-bit mappings and transforms the problem into a higher-order unconstrained binary optimization (HUBO) formulation. To address this problem, this paper presents a hybrid quantum-classical detection framework that leverages a warm-start linear-ramp Quantum Approximate Optimization Algorithm (WSLR-QAOA) for solving the resulting HUBO problem. A structured warm-start based on a low-rank semidefinite relaxation, solved via a block coordinate descent (BCD) method, provides an efficient and high-quality initialization, while a linear ramp parameterization guides the QAOA optimization. Simulation results show that the proposed framework outperforms classical methods in terms of symbol error rate (SER) and converges faster than standard QAOA, while achieving performance close to the optimal ML detector. Furthermore, the WSLR-QAOA algorithm is validated on actual IBM quantum hardware, where it achieves near-ML performance at low SNR and maintains competitive accuracy at higher SNR despite moderate degradation due to hardware noise. This demonstrates the practical potential of the HUBO-based WSLR-QAOA algorithm for large-scale MIMO data detection.

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arXiv2026

Quantum Radar for ISAC: Sum-Rate Optimization

Integrated sensing and communication (ISAC) is emerging as a key enabler for spectrum-efficient and hardware-converged wireless networks. However, classical radar systems within ISAC architectures face fundamental limitations under low signal power and high-noise conditions. This paper proposes a novel framework that embeds quantum illumination radar into a base station to simultaneously support full-duplex classical communication and quantum-enhanced target detection. The resulting integrated quantum sensing and classical communication (IQSCC) system is optimized via a sum-rate maximization formulation subject to radar sensing constraints. The non-convex joint optimization of transmit power and beamforming vectors is tackled using the successive convex approximation technique. Furthermore, we derive performance bounds for classical and quantum radar protocols under the statistical detection theory, highlighting the quantum advantage in low signal-to-interference-plus-noise ratio regimes. Simulation results demonstrate that the proposed IQSCC system achieves a higher communication throughput than the conventional ISAC baseline while satisfying the sensing requirement.

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arXiv2026

Quantum CDMA-based Continuous Variable Quantum Key Distribution using Chaotic Phase Shifters

We present a quantum code-division multiple-access (q-CDMA) framework for multiuser continuous-variable quantum key distribution (CV-QKD) over a shared quantum channel. The proposed architecture employs chaotic phase shifters to encode and decode quantum states, enabling efficient multiplexing and demultiplexing of signals generated by multiple transmitters. In this scheme, quantum states from different users are chaotically phase-encoded and combined through a beam splitter network before transmission. At the receiver, synchronized chaotic phase shifters are used for decoding, followed by an inverse beam splitter structure to recover the individual user signals. This chaotic synchronization allows reliable state recovery and secure key establishment between each sender-receiver pair. For an arbitrary number of users, we derive the input-output quadrature relations describing the multiuser q-CDMA CV-QKD system. Using this model, we evaluate the achievable secret key rate under collective attacks with reverse reconciliation. We further investigate the impact of key system parameters including the correction factor, multiuser interference noise, environmental noise, and channel transmittance. A comparison between the asymptotic and finite-size regimes is also presented to highlight the associated performance trade-offs. These results provide a theoretical framework for assessing the performance of q-CDMA-based CV-QKD and support the development of scalable and secure multiuser quantum communication networks.

quant-ph↗

arXiv2026

Rydberg Vision via frugal Quantum Image Fingerprinting

Gate-based quantum image processing is constrained by qubit scarcity and the high overhead of quantum state preparation, limiting its applicability to realistic geometric data. We introduce a quantum-native framework for image matching on neutral-atom analog quantum computers that advances our earlier Sparse-Dots Representation (SDR) approach. A classical pre-processing pipeline -- Sobel edge extraction followed by the Ramer--Douglas--Peucker (RDP) algorithm -- converts an input image into a geometrically faithful Sparse-Dots point cloud of substantially fewer atoms. This atom layout is virtually embedded into the programmable tweezer array of QuEra's Aquila device via its Bloqade SDK, where the image geometry is encoded physically in the distance-dependent van der Waals interaction term of the Rydberg Hamiltonian. After time-evolution, we extract the many-body fingerprint of each image using two observables -- the Pearson-normalized two-site correlation matrix which encodes the blockade-induced correlation structure of the quantum state, and the two-dimensional static structure factor evaluated on a fixed wavevector grid, yielding a fingerprint vector of constant length regardless of atom count. In Stage~1, image matching is performed by cosine similarity on the fingerprint vectors, a scale-invariant metric appropriate for Fourier-domain descriptors. In Stage~2, this approach is extended to quantum reservoir computing~(QRC) to enable machine learning via dramatically reduced training data and training cycles, as a preliminary proof-of-concept. Simulations using the Bloqade software stack confirm successful matching of industrial objects, often with fewer than 24 atoms. To our knowledge, this constitutes the first application of the static structure factor -- a condensed-matter quantum observable -- as an image retrieval descriptor in an analog quantum computing context.

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arXiv2026

Free-space and Satellite-Based Quantum Communication: Principles, Implementations, and Challenges

Satellite-based quantum communications represent a critical advancement in the pursuit of secure, global-scale quantum networks. Leveraging the principles of quantum mechanics, these systems offer unparalleled security through Quantum Key Distribution (QKD) and other quantum communication protocols. This review provides a comprehensive overview of the current state of satellite-based quantum communications, focusing on the evolution from terrestrial to space-based systems. We explore the distinct advantages and challenges of discrete-variable (DV) and continuous-variable (CV) quantum communication technologies in the context of satellite deployments. The paper also discusses key milestones such as the successful implementation of quantum communication via the Micius satellite and outlines the primary challenges, including atmospheric turbulence and the development of quantum repeaters, that must be addressed to achieve a global quantum internet. This review aims to consolidate recent advancements in the field, providing insights and perspectives on the future directions and potential innovations that will drive the continued evolution of satellite-based quantum communications.

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arXiv2026

Rydberg Atomic RF Sensor-based Quantum Radar

Rydberg atom-based RF sensors offer distinct advantages over conventional dipole antennas for electric field detection. This paper presents a system model and performance analysis of a Rydberg atom-based quantum radar, which employs optical readout via lasers and photon detectors instead of circuit-based receivers. We derive the signal-to-noise ratio (SNR), compare it with classical radar, and estimate Doppler frequency using an invariant function-based method. Simulations show that the quantum radar achieves higher SNR and lower RMSE in velocity estimation than conventional radar.

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arXiv2025

Myopic Entropy Scheduling for Ramsey Magnetometry

This paper presents an entropy based adaptive measurement sequence strategy for quantum sensing of magnetic fields. To physically ground our ideas we consider a sensor employing a nitrogen vacancy center in diamond, however our approach is applicable to other quantum sensor arrangements. The sensitivity and accuracy of these sensors typically rely on long sequences of rapidly occurring measurements. We introduce a new technique for designing these measurement sequences aimed at reducing the number of measurements required for a specified accuracy as measured by entropy, by selecting measurement parameters that optimally reduce entropy at each measurement. We compare, via simulation, the efficiency and sensitivity of our new method with several existing measurement sequence design strategies. Our results show quantifiable improvements in sensing performance. We also show analytically that our entropy reduction approach, reduces, under certain simplified conditions, to a well-known and widely used measurement strategy.

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arXiv2025

A Qubit-Efficient Hybrid Quantum Encoding Mechanism for Quantum Machine Learning

Efficiently embedding high-dimensional datasets onto noisy and low-qubit quantum systems is a significant barrier to practical Quantum Machine Learning (QML). Approaches such as quantum autoencoders can be constrained by current hardware capabilities and may exhibit vulnerabilities to reconstruction attacks due to their invertibility. We propose Quantum Principal Geodesic Analysis (qPGA), a novel, non-invertible method for dimensionality reduction and qubit-efficient encoding. Executed classically, qPGA leverages Riemannian geometry to project data onto the unit Hilbert sphere, generating outputs inherently suitable for quantum amplitude encoding. This technique preserves the neighborhood structure of high-dimensional datasets within a compact latent space, significantly reducing qubit requirements for amplitude encoding. We derive theoretical bounds quantifying qubit requirements for effective encoding onto noisy systems. Empirical results on MNIST, Fashion-MNIST, and CIFAR-10 show that qPGA preserves local structure more effectively than both quantum and hybrid autoencoders. Additionally, we demonstrate that qPGA enhances resistance to reconstruction attacks due to its non-invertible nature. In downstream QML classification tasks, qPGA can achieve over 99% accuracy and F1-score on MNIST and Fashion-MNIST, outperforming quantum-dependent baselines. Initial tests on real hardware and noisy simulators confirm its potential for noise-resilient performance, offering a scalable solution for advancing QML applications.

quant-ph↗

arXiv2025

Quantum code division multiple access based continuous-variable quantum key distribution

In this paper, we propose a quantum code division multiple access (q-CDMA) based continuous-variable quantum key distribution (CV-QKD) system. In the proposed system, the quantum states of two senders ($\text{Alice}_{1,2}$) are chaotically encoded through chaotic phase shifters and then transmitted over a quantum channel. At the receiver, the quantum states are decoded via chaos synchronization to separate the quantum states sent by the different senders and received by the two receivers ($\text{Bob}_{1,2}$) separately. We characterize the input-output relation of the quadrature between the two senders and receivers and then analyze the secret key rate (SKR) of the q-CDMA-based CV-QKD system. Our numerical results reveal that the q-CDMA approach can significantly enhance the SKR for both users when compared to the single-user case without the q-CDMA approach.

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arXiv2024

Error-Mitigated Quantum Random Access Memory

As an alternative to quantum error correction, quantum error mitigation methods, including Zero-Noise Extrapolation (ZNE), have been proposed to alleviate run-time errors in current noisy quantum devices. In this work, we propose a modified version of ZNE that provides for a significant performance enhancement on current noisy devices. Our modified ZNE method extrapolates to zero-noise data by evaluating groups of noisy data obtained from noise-scaled circuits and selecting extrapolation functions for each group with the assistance of estimated noisy simulation results. To quantify enhancement in a real-world quantum application, we embed our modified ZNE in Quantum Random Access Memory (QRAM) - a memory system important for future quantum networks and computers. Our new ZNE-enhanced QRAM designs are experimentally implemented on a 27-qubit noisy superconducting quantum device, the results of which demonstrate QRAM fidelity can be improved significantly relative to traditional ZNE usage. Our results demonstrate the critical role the extrapolation function plays in ZNE - judicious choice of that function on a per-measurement basis can make the difference between a quantum application being functional or non-functional.

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arXiv2024

Hybrid Quantum Neural Network based Indoor User Localization using Cloud Quantum Computing

This paper proposes a hybrid quantum neural network (HQNN) for indoor user localization using received signal strength indicator (RSSI) values. We use publicly available RSSI datasets for indoor localization using WiFi, Bluetooth, and Zigbee to test the performance of the proposed HQNN. We also compare the performance of the HQNN with the recently proposed quantum fingerprinting-based user localization method. Our results show that the proposed HQNN performs better than the quantum fingerprinting algorithm since the HQNN has trainable parameters in the quantum circuits, whereas the quantum fingerprinting algorithm uses a fixed quantum circuit to calculate the similarity between the test data point and the fingerprint dataset. Unlike prior works, we also test the performance of the HQNN and quantum fingerprint algorithm on a real IBM quantum computer using cloud quantum computing services. Therefore, this paper examines the performance of the HQNN on noisy intermediate scale (NISQ) quantum devices using real-world RSSI localization datasets. The novelty of our approach lies in the use of simple feature maps and ansatz with fewer neurons, alongside testing on actual quantum hardware using real-world data, demonstrating practical applicability in real-world scenarios.

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arXiv2024

Error-Mitigated Multi-Layer Quantum Routing

Due to the numerous limitations of current quantum devices, quantum error mitigation methods become potential solutions for realizing practical quantum applications in the near term. Zero-Noise Extrapolation (ZNE) and Clifford Data Regression (CDR) are two promising quantum error mitigation methods. Based on the characteristics of these two methods, we propose a new method named extrapolated CDR (eCDR). To benchmark our method, we embed eCDR into a quantum application, specifically multi-layer quantum routing. Quantum routers direct a quantum signal from one input path to a quantum superposition of multiple output paths and are considered important elements of future quantum networks. Multi-layer quantum routers extend the scalability of quantum networks by allowing for further superposition of paths. We benchmark the performance of multi-layer quantum routers implemented on current superconducting quantum devices instantiated with the ZNE, CDR, and eCDR methods. Our experimental results show that the new eCDR method significantly outperforms ZNE and CDR for the 2-layer quantum router. Our work highlights how new mitigation methods built from different components of pre-existing methods, and designed with a core application in mind, can lead to significant performance enhancements.

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