arXiv subjects

Bikash K. Behera

Publications and source records attributed to Bikash K. Behera.

At least 19 recordsLinked to original sources

arXiv2026

QSVM-RQNN: Low-Qubit Recurrent Quantum Similarity Learning for Condition Monitoring and Fault Classification

In the Noisy Intermediate-Scale Quantum (NISQ) era, limited qubit availability and hardware noise constrain the practical deployment of quantum machine learning (QML). Existing quantum neural network (QNN) and quantum convolutional neural network (QCNN) architectures often require increasing quantum resources as the input dimension grows, limiting scalability on near-term devices. We propose QSVM-RQNN, a low-qubit framework integrating centroid-based Quantum Support Vector Machine (QSVM) similarity learning with Recurrent Quantum Neural Networks (RQNNs) for fault classification. The framework reduces the feature space using principal component analysis (PCA), partitions the reduced representation into sequential timesteps, and processes them using a compact three-qubit recurrent quantum architecture with shared parameters. Two complementary variants are developed: QSVM-RQNN-V1 performs class-conditioned joint quantum encoding of input and centroid segments, whereas QSVM-RQNN-V2 performs recurrent learning over timestep-wise quantum similarity representations. Experimental evaluation on multiple fault diagnosis datasets shows competitive and, in several cases, state-of-the-art performance compared with QSVM, QNN, QCNN, QSVM-QNN, QSVM-QCNN, and RQNN models. The proposed architectures provide favorable performance-efficiency trade-offs, improved recall, and enhanced fault detection on highly imbalanced datasets. These results demonstrate that integrating centroid-based quantum similarity learning with low-qubit recurrent representation learning provides an effective and scalable approach to condition monitoring and fault classification on resource-constrained NISQ devices.

quant-ph↗

arXiv2026

QMIMO: Circuit Based Quantum MIMO Design with Variational Receiver

This paper investigates a quantum extension of classical Multiple-Input Multiple-Output (MIMO) communication in which the conventional linear channel model is replaced by a parameterized multi-qubit unitary transformation. Within this framework, interference is represented through coherent quantum interactions rather than additive signal coupling. To recover transmitted information, a Variational Quantum Circuit (VQC) receiver is introduced that learns an approximate inverse channel transformation through supervised variational optimization. The proposed system is evaluated under realistic noisy intermediate-scale quantum (NISQ) conditions incorporating depolarizing noise, thermal relaxation, and measurement imperfections, and its performance is compared with that of standard classical detection methods. The results reveal a trade-off between the two approaches: classical detectors achieve substantially lower bit-error rates across much of the investigated parameter range but exhibit pronounced performance degradation for specific channel configurations, whereas the VQC receiver maintains a more uniform error profile as channel complexity increases, albeit at a higher average BER. These findings suggest that variational quantum receivers are not a direct replacement for classical detection methods, but rather a complementary approach that may offer increased performance stability in communication scenarios characterized by strong coupling and complex interference patterns.

quant-ph↗

arXiv2026

QARIMA: A Quantum Approach To Classical Time Series Analysis

We present QARIMA, a quantum state-similarity-based reconstruction of the classical ARIMA modelling pipeline. Rather than using a quantum circuit as a standalone forecaster, QARIMA preserves ARIMA's interpretable forecasting structure while reformulating its core building blocks through analogous quantum-compatible modules. The framework integrates quantum differencing assessment, QACF/QPACF lag discovery, compact-swap-test state projection, swap-test/VQC-based AR and MA coefficient estimation, and weak-lag refinement within a single ARIMA forecasting workflow. QACF and QPACF serve the functional roles of ACF and PACF for MA and AR lag discovery, but construct lag relevance through quantum measurement geometry rather than direct classical correlation. Given screened candidate orders $(p,d,q)$, AR and MA coefficients are estimated through state-alignment losses incorporating cosine alignment, entropy regularization, phase correction, and norm control. We evaluate QARIMA across environmental, climatic, industrial, and weather time-series datasets using rolling-origin out-of-sample testing against automated classical ARIMA baselines, with performance assessed through MSE, MAPE, and Diebold--Mariano tests. The results show that quantum state-similarity modules can produce competitive and, in several cases, improved forecasting behaviour while preserving ARIMA's transparency, modularity, and interpretability. QARIMA therefore establishes a distinct pathway for quantum-enhanced statistical forecasting: its modules remain functionally analogous to classical ARIMA subroutines, but are not algebraic replicas; they serve the same modelling roles through state overlap, projection, and measurement-driven parameter estimation.

quant-ph↗

arXiv2026

Robust Pretty Good Measurement via Hybrid Classical-Quantum Pseudoinverse Approximation and Circuit-Level Realization

Pretty Good Measurement (PGM) is a near-optimal strategy for quantum state discrimination, but its practical realization becomes unstable when the ensemble operator is singular or ill-conditioned. We introduce a numerically robust PGM formulation based on the Moore-Penrose pseudoinverse, replacing the standard inverse square root with a threshold-regularized variant that remains well-defined across different spectral regimes. We develop a hybrid classical-quantum framework that combines pseudoinverse-based spectral preprocessing with quantum circuit realizations using block-encoding and spectral-transformation techniques. The framework incorporates support awareness, yielding physically meaningful measurement operators even in rank-deficient cases, and employs oblivious amplitude amplification to improve circuit-level success probabilities. Extensive numerical and circuit-level simulations show close agreement between theoretical predictions and quantum circuit outputs. Experiments on synthetic and real datasets, including ill-conditioned and degenerate scenarios, demonstrate stable discrimination performance where standard PGM becomes numerically unstable. The results establish a practical hybrid classical-quantum framework for robust quantum state discrimination and extend previous circuit-based implementations of the PGM testing stage toward pseudoinverse-aware measurement design.

quant-ph↗

arXiv2026

Noisy-QSMOTE: Robustness Analysis of Quantum SMOTE under Quantum-Inspired Noise for Condition Monitoring and Fault Classification in Industrial and Energy Systems

Imbalanced datasets remain a major challenge in industrial condition monitoring and fault diagnosis, often causing machine-learning models to favor majority classes while underrepresenting minority fault conditions. This work investigates the Quantum Synthetic Minority Oversampling Technique (QSMOTE) through three stages: (i) baseline evaluation on the original imbalanced datasets, (ii) assessment after QSMOTE-based balancing, and (iii) analysis of QSMOTE under quantum-inspired perturbations. Unlike conventional robustness studies, the considered noise channels are injected directly into the compact-swap-test-based similarity estimation process used during synthetic sample generation, influencing overlap estimation, angle computation, and the generated minority samples. Experiments are conducted on four multi-class datasets: the Solar Panel Image Dataset (SPID), the CWRU Bearing Dataset (CWRUBD), the Engine Failure Detection Dataset (EFDD), and the Industrial Fault Detection Dataset (IFDD). Performance is evaluated using Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), and Naive Bayes (NB) classifiers. The results show that QSMOTE effectively reduces class imbalance and substantially improves the performance of non-linear classifiers, with gains of up to 170% on EFDD and accuracies exceeding 0.99 on IFDD. Further analysis under bit-flip, phase-flip, bit-phase-flip, depolarizing, amplitude damping, and phase damping channels demonstrates how perturbations introduced during similarity estimation propagate through synthetic sample generation and influence downstream classification performance. The proposed framework provides a practical approach for studying both imbalance mitigation and noisy quantum-inspired oversampling in industrial and energy-system applications.

quant-ph↗

arXiv2026

QSMOTE-PGM/kPGM: QSMOTE Based PGM and kPGM for Imbalanced Dataset Classification

Quantum-inspired machine learning (QiML) employs mathematical principles from quantum theory, such as Hilbert-space representations and quantum state discrimination, to enhance classical learning algorithms. In this work, we investigate the integration of Quantum Synthetic Minority Oversampling Technique (QSMOTE) variants with two quantum-inspired classifiers: the Pretty Good Measurement (PGM) classifier and the kernelized Pretty Good Measurement (KPGM) classifier. We propose and analyze three QSMOTE variants, namely KNN-based, Fidelity-based, and Margin-based QSMOTE, designed to improve minority-class representation in imbalanced datasets through quantum-inspired similarity and sampling mechanisms. A unified theoretical and empirical comparison of PGM and KPGM is presented under amplitude and stereo encoding strategies with multiple quantum copies. Experimental evaluations on the Telco Customer Churn dataset demonstrate that the proposed quantum-inspired approaches consistently outperform a classical Random Forest baseline, particularly in terms of recall and balanced F1-score. Among all configurations, PGM with stereo encoding and n_{copies}=2 achieves the best performance with an accuracy of 0.8512 and an F1-score of 0.8234, while KPGM exhibits competitive and more stable behavior across different QSMOTE variants, reaching accuracies of 0.8511 under stereo encoding and 0.8483 under amplitude encoding. The results further show that increasing the number of quantum copies systematically improves classification performance, especially for minority-class detection. This work highlights the effectiveness of combining quantum-inspired oversampling and classification strategies for imbalanced learning, while providing practical insights into the complementary strengths of measurement-based and kernel-based quantum-inspired machine learning frameworks.

cs.LG↗

arXiv2026

Unsharp Measurement with Adaptive Gaussian POVMs for Quantum-Inspired Image Processing

We propose a data-adaptive probabilistic intensity remapping framework for structure-preserving transformation of grayscale images. The suggested method formulates intensity transformation as a continuous, data-driven remapping process, in contrast to traditional histogram-based techniques that rely on hard thresholding and generate piecewise-constant mappings. The image statistics yield representative intensity values, and Gaussian-based weighting methods probabilistically allocate each pixel to several components. Smooth transitions while preserving structural features are achieved by computing the output intensity as an expectation over these components. A smooth transition from soft probabilistic remapping to hard assignment is made possible by the introduction of a nonlinear sharpening parameter $γ$ to regulate the degree of localization. This offers clear control over the trade-off between intensity discrimination and smoothing. Furthermore, the resolution of the remapping function is determined by the number of components $k$. When compared to thresholding-based methods, experimental results on standard benchmark images show that the suggested method achieves better structural fidelity and controlled information reduction as measured by PSNR, SSIM, and entropy. Overall, by allowing continuous, probabilistic intensity modifications, the framework provides a robust and efficient substitute for discrete thresholding.

quant-ph↗

arXiv2026

Quantum Computing of Phonon Spectra and Thermal Properties of Crystalline Solids

Variational quantum algorithms offer a promising framework for solving eigenvalue problems on near-term quantum hardware, yet their applicability beyond electronic structure calculations remains relatively unexplored. In this work, we investigate the quantum computing of lattice vibrational and thermodynamical properties by applying the variational quantum eigensolver and variational quantum deflation to phonon Hamiltonians derived from first-principles force constants obtained using density functional theory. The mass-weighted dynamical matrix is mapped onto a qubit-encoded Hermitian operator, enabling computation of the full set of acoustic and optical phonon branches of crystalline silicon and graphene using a reduced qubit register and direct benchmarking against classical diagonalization. The quantum-computed phonon spectrum is further used to evaluate vibrational entropy, constant-volume specific heat, and thermal expansion coefficient, reproducing expected low-temperature quantum behavior and the high-temperature Dulong-Petit limit. We further demonstrate that combined error mitigation strategies help recover phonon dispersions and thermodynamic behavior consistent with expected trends on near-term quantum hardware. Although classical phonon methods remain computationally superior, our results establish phonon-based thermodynamics as a stringent and physically transparent benchmark for assessing variational quantum algorithms on near-term quantum devices.

cond-mat.mtrl-sci↗

arXiv2026

Quantum-Inspired Geometric Classification with Correlation Group Structures and VQC Decision Modeling

We propose a geometry-driven quantum-inspired classification framework that integrates Correlation Group Structures (CGR), compact SWAP-test-based overlap estimation, and selective variational quantum decision modelling. Rather than directly approximating class posteriors, the method adopts a geometry-first paradigm in which samples are evaluated relative to class medoids using overlap-derived Euclidean-like and angular similarity channels. CGR organizes features into anchor-centered correlation neighbourhoods, generating nonlinear, correlation-weighted representations that enhance robustness in heterogeneous tabular spaces. These geometric signals are fused through a non-probabilistic margin-based fusion score, serving as a lightweight and data-efficient primary classifier for small-to-moderate datasets. On Heart Disease, Breast Cancer, and Wine Quality datasets, the fusion-score classifier achieves 0.8478, 0.8881, and 0.9556 test accuracy respectively, with macro-F1 scores of 0.8463, 0.8703, and 0.9522, demonstrating competitive and stable performance relative to classical baselines. For large-scale and highly imbalanced regimes, we construct compact Delta-distance contrastive features and train a variational quantum classifier (VQC) as a nonlinear refinement layer. On the Credit Card Fraud dataset (0.17% prevalence), the Delta + VQC pipeline achieves approximately 0.85 minority recall at an alert rate of approximately 1.31%, with ROC-AUC 0.9249 and PR-AUC 0.3251 under full-dataset evaluation. These results highlight the importance of operating-point-aware assessment in rare-event detection and demonstrate that the proposed hybrid geometric-variational framework provides interpretable, scalable, and regime-adaptive classification across heterogeneous data settings.

quant-ph↗

arXiv2026

Resisting Quantum Key Distribution Attacks Using Quantum Machine Learning

The emergence of quantum computing poses significant risks to the security of modern communication networks as it breaks today's public-key cryptographic algorithms. Quantum Key Distribution (QKD) offers a promising solution by harnessing the principles of quantum mechanics to establish secure keys. However, practical QKD implementations remain vulnerable to hardware imperfections and advanced attacks such as Photon Number Splitting and Trojan-Horse attacks. In this work, we investigate the potential of quantum machine learning (QML) to detect QKD attacks. In particular, we propose a Hybrid Quantum Long Short-Term Memory (QLSTM) model to improve detection performance. By combining quantum-enhanced learning with classical deep learning, the model captures temporal patterns in QKD data, improving detection accuracy. To evaluate the proposed model, we introduce a QKD dataset that simulates typical operations along with multiple attack scenarios, including Intercept-and-Resend, Photon-Number Splitting, Trojan-Horse, Detector Blinding, and Combined attacks. The dataset includes Quantum Bit Error Rate (QBER), signal and decoy loss rates, and time-based metrics. Our results demonstrate the promising performance of the quantum machine learning approach compared to classical models. The proposed Hybrid QLSTM achieved an accuracy of 94.7% after 50 training epochs. The evaluation is conducted on a semi-realistic, simulation-generated decoy-state BB84 dataset, and the reported performance should be interpreted as a proof-of-concept rather than a final assessment on field-deployed QKD systems.

cs.CR↗

arXiv2025

QNN-QRL: Quantum Neural Network Integrated with Quantum Reinforcement Learning for Quantum Key Distribution

Quantum key distribution (QKD) has emerged as a critical component of secure communication in the quantum era, ensuring information-theoretic security. Despite its potential, there are issues in optimizing key generation rates, enhancing security, and incorporating QKD into practical implementations. This research introduces a unique framework for incorporating quantum machine learning (QML) algorithms, notably quantum reinforcement learning (QRL) and quantum neural networks (QNN), into QKD protocols to improve key generation performance. Here, we present two novel QRL-based algorithms, QRL-V.1 and QRL-V.2, and propose the standard BB84 and B92 protocols by integrating QNN algorithms to form QNN-BB84 and QNN-B92. Furthermore, we combine QNN with the above QRL-based algorithms to produce QNN-QRL-V.1 and QNN-QRL-V.2. These unique algorithms and established protocols are compared using evaluation metrics such as accuracy, precision, recall, F1 score, confusion matrices, and ROC curves. The results from the QNN-based proposed algorithms show considerable improvements in key generation quality. The existing and proposed models are investigated in the presence of different noisy channels to check their robustness. The proposed integration of QML algorithms into QKD protocols and their noisy analysis create a new paradigm for efficient key generation, which advances the practical implementation of QKD systems.

quant-ph↗

arXiv2025

Quantum Simulation and Energy Estimation for Discretized Anharmonic oscillator

Anharmonic potential quantum system play crucial role in physics as they provide a more realistic description of oscillatory phenomena, which often deviate from the idealized harmonic model. However, simulating such system on classical computers is highly challenging due to nonlinear interactions, large state spaces, and the exponential scaling of memory and computational resources. In this work, quantum simulation is employed to model a quantum anharmonic oscillator (QAHO) using a 3-qubit system implemented on IBM's Quantum Experiences platform. A quantum circuit with a filter-based design and Toffoli gates is constructed to track quantum state evolution, capturing key phenomena like quantum revival. The framework is further extended to n-qubit system to enhance resolution and scalability. For energy estimation, the Variational Quantum Eigensolver (VQE) with a TwoLocal ansatz and variational Quantum Deflation (VQD), are used to compute ground and excited state energies. The proposed approach achieves high accuracy with an error of only 1.11% compared to exact methods. Notably, VQE outperforms classical approximations such as perturbation theory (error 6.71%) and the Wentzel-Kramers-Brillouin (WKB) approximation(error 5.36%), yielding more precise energy values. These results highlight the potential of quantum simulation and VQD as effective tools for investigating complex quantum system, paving the way for future application in quantum chemistry and materials science as quantum hardware continues to advance.

quant-ph↗

arXiv2025

Excitation Gaps of Ground and Excited State Energy of the Fermi-Hubbard Model Using Variational Quantum Eigensolver

The Hubbard model is a challenging quantum many-body problem and serves as a benchmark for quantum computing research. Accurate computation of its ground and excited state energies is essential for understanding correlated electron systems. In this study, the ground, first, and second excited state energies of 4$\times$1 and 2$\times$2 Hubbard lattices are obtained using a newly designed ansatz circuit. The ansatz is constructed by combining concepts from the Hamiltonian Variational Ansatz (HVA) and the Number-Preserving Ansatz (NPA). A hybrid optimization strategy is applied, where COBYLA is used for coarse convergence and L-BFGS for fine-tuning. The resulting energies are evaluated, and the corresponding physical properties of the systems are analyzed through phase diagrams of the energy excitation gaps for different charge and spin configurations.

quant-ph↗

arXiv2025

Quantum Machine Learning for Energy-Efficient 5G-Enabled IoMT Healthcare Systems: Enhancing Data Security and Processing

Energy-efficient healthcare systems are becoming increasingly critical for Industry 5.0 as the Internet of Medical Things (IoMT) expands, particularly with the integration of 5G technology. 5G-enabled IoMT systems allow real-time data collection, high-speed communication, and enhanced connectivity between medical devices and healthcare providers. However, these systems face energy consumption and data security challenges, especially with the growing number of connected devices operating in Industry 5.0 environments with limited power resources. Quantum computing integrated with machine learning (ML) algorithms, forming quantum machine learning (QML), offers exponential improvements in computational speed and efficiency through principles such as superposition and entanglement. In this paper, we propose and evaluate three QML algorithms, which are UU†, variational UU†, and UU†-quantum neural networks (QNN) for classifying data from four different datasets: 5G-South Asia, Lumos5G 1.0, WUSTL EHMS 2020, and PS-IoT. Our comparative analysis, using various evaluation metrics, reveals that the UU†-QNN method not only outperforms the other algorithms in the 5G-South Asia and WUSTL EHMS 2020 datasets, achieving 100% accuracy, but also aligns with the human-centric goals of Industry 5.0 by allowing more efficient and secure healthcare data processing. Furthermore, the robustness of the proposed quantum algorithms is verified against several noisy channels by analyzing accuracy variations in response to each noise model parameter, which contributes to the resilience aspect of Industry 5.0. These results offer promising quantum solutions for 5G-enabled IoMT healthcare systems by optimizing data classification and reducing power consumption while maintaining high levels of security even in noisy environments.

quant-ph↗

arXiv2025

QSVM-QNN: Quantum Support Vector Machine Based Quantum Neural Network Learning Algorithm for Brain-Computer Interfacing Systems

A brain-computer interface (BCI) system enables direct communication between the brain and external devices, offering significant potential for assistive technologies and advanced human-computer interaction. Despite progress, BCI systems face persistent challenges, including signal variability, classification inefficiency, and difficulty adapting to individual users in real time. In this study, we propose a novel hybrid quantum learning model, termed QSVM-QNN, which integrates a Quantum Support Vector Machine (QSVM) with a Quantum Neural Network (QNN), to improve classification accuracy and robustness in EEG-based BCI tasks. Unlike existing models, QSVM-QNN combines the decision boundary capabilities of QSVM with the expressive learning power of QNN, leading to superior generalization performance. The proposed model is evaluated on two benchmark EEG datasets, achieving high accuracies of 0.990 and 0.950, outperforming both classical and standalone quantum models. To demonstrate real-world viability, we further validated the robustness of QNN, QSVM, and QSVM-QNN against six realistic quantum noise models, including bit flip and phase damping. These experiments reveal that QSVM-QNN maintains stable performance under noisy conditions, establishing its applicability for deployment in practical, noisy quantum environments. Beyond BCI, the proposed hybrid quantum architecture is generalizable to other biomedical and time-series classification tasks, offering a scalable and noise-resilient solution for next-generation neurotechnological systems.

quant-ph↗

arXiv2025

Quantum-Inspired Optimization Process for Data Imputation

Data imputation is a critical step in data pre-processing, particularly for datasets with missing or unreliable values. This study introduces a novel quantum-inspired imputation framework evaluated on the UCI Diabetes dataset, which contains biologically implausible missing values across several clinical features. The method integrates Principal Component Analysis (PCA) with quantum-assisted rotations, optimized through gradient-free classical optimizers -COBYLA, Simulated Annealing, and Differential Evolution to reconstruct missing values while preserving statistical fidelity. Reconstructed values are constrained within +/-2 standard deviations of original feature distributions, avoiding unrealistic clustering around central tendencies. This approach achieves a substantial and statistically significant improvement, including an average reduction of over 85% in Wasserstein distance and Kolmogorov-Smirnov test p-values between 0.18 and 0.22, compared to p-values > 0.99 in classical methods such as Mean, KNN, and MICE. The method also eliminates zero-value artifacts and enhances the realism and variability of imputed data. By combining quantum-inspired transformations with a scalable classical framework, this methodology provides a robust solution for imputation tasks in domains such as healthcare and AI pipelines, where data quality and integrity are crucial.

quant-ph↗

arXiv2025

QFDNN: A Resource-Efficient Variational Quantum Feature Deep Neural Networks for Fraud Detection and Loan Prediction

Social financial technology focuses on trust, sustainability, and social responsibility, which require advanced technologies to address complex financial tasks in the digital era. With the rapid growth in online transactions, automating credit card fraud detection and loan eligibility prediction has become increasingly challenging. Classical machine learning (ML) models have been used to solve these challenges; however, these approaches often encounter scalability, overfitting, and high computational costs due to complexity and high-dimensional financial data. Quantum computing (QC) and quantum machine learning (QML) provide a promising solution to efficiently processing high-dimensional datasets and enabling real-time identification of subtle fraud patterns. However, existing quantum algorithms lack robustness in noisy environments and fail to optimize performance with reduced feature sets. To address these limitations, we propose a quantum feature deep neural network (QFDNN), a novel, resource efficient, and noise-resilient quantum model that optimizes feature representation while requiring fewer qubits and simpler variational circuits. The model is evaluated using credit card fraud detection and loan eligibility prediction datasets, achieving competitive accuracies of 82.2% and 74.4%, respectively, with reduced computational overhead. Furthermore, we test QFDNN against six noise models, demonstrating its robustness across various error conditions. Our findings highlight QFDNN potential to enhance trust and security in social financial technology by accurately detecting fraudulent transactions while supporting sustainability through its resource-efficient design and minimal computational overhead.

quant-ph↗

arXiv2025

A Blockchain-based Quantum Binary Voting for Decentralized IoT Towards Industry 5.0

Industry 5.0 depends on intelligence, automation, and hyperconnectivity operations for effective and sustainable human-machine collaboration. Pivotal technologies like the Internet of Things (IoT) enable this by facilitating connectivity and data-driven decision-making between cyber-physical devices. As IoT devices are prone to cyberattacks, they can use blockchain to improve transparency in the network and prevent data tampering. However, in some cases, even blockchain networks are vulnerable to Sybil and 51% attacks. This has motivated the development of quantum blockchains that are more resilient to such attacks as they leverage post-quantum cryptographic protocols and secure quantum communication channels. In this work, we develop a quantum binary voting algorithm for the IoT-quantum blockchain frameworks that enables inter-connected devices to reach a consensus on the validity of transactions, even in the presence of potential faults or malicious actors. The correctness of the voting protocol is provided in detail, and the results show that it guarantees the achievement of a consensus securely against all kinds of significant external and internal attacks concerning quantum bit commitment, quantum blockchain, and quantum Byzantine agreement. We also provide an implementation of the voting algorithm with the quantum circuits simulated on the IBM Quantum platform and Simulaqron library.

quant-ph↗