arXiv subjects

M. R. C. Mahdy

Publications and source records attributed to M. R. C. Mahdy.

At least 19 recordsLinked to original sources

arXiv2026

Ultra-BroadBand Electromagnetic Control Using a Triple Circular Ring Metasurface: Surface Wave Propagation, Beam Steering, and RCS reduction (50-100 Ghz)

Traditional metasurfaces often face challenges in achieving broadband functionality and dynamic adaptability, limiting their use in advanced electromagnetic systems. This paper presents a triple circular ring metasurface designed for multifunctional electromagnetic applications, including surface wave propagation, beam shaping, and ultra-broadband radar cross-section (RCS) reduction. The proposed structure uses a cost-effective FR-4 substrate and demonstrates strong electromagnetic reflection characteristics across 50-100 GHz. Except near 91 GHz, the metasurface exhibits amplitude and phase responses comparable to a conventional copper plate while maintaining efficient surface wave propagation. Significant electric and magnetic field amplitudes of nearly 1 V/m and 5x10^-3 A/m are sustained across the surface, unlike a standard copper plate. The metasurface also redirects incident energy toward a predefined direction of 67 degrees in the phi plane while minimizing radiation over a 360-degree angular range. In addition, it achieves a stable monostatic RCS reduction from -40 dB to -30 dB across a broad frequency range, outperforming conventional copper structures. Numerical simulations validate the proposed design. The results demonstrate strong potential for stealth technology, radar systems, and next-generation wireless communications.

physics.optics↗

arXiv2025

An Idea of Implementing Photonic Space-Time Crystals Using Metasurfaces

Photonic space time crystals (PSTCs) are emerging materials characterized by periodic variations in electromagnetic parameters with respect to both space and time. To date, research on PSTCs remains theoretical, with no practical realization reported. This article presents the practical realization of PSTCs through the design and simulation of a 2D metasurface-based photonic space-time crystal. It illustrates the characteristics of energy bandgaps, momentum bandgaps, and mixed energy momentum bandgap eigenmodes within the dispersion relation of these crystals. Additionally, the article details the properties of second order exceptional points that occur when two bandgaps overlap under specific conditions. The application of the metasurface based PSTC in 6G wireless communications is also demonstrated. This work aims to advance the understanding of PSTCs and their potential applications.

physics.app-ph↗

arXiv2025

CQ CNN: A Hybrid Classical Quantum Convolutional Neural Network for Alzheimer's Disease Detection Using Diffusion Generated and U Net Segmented 3D MRI

The detection of Alzheimer disease (AD) from clinical MRI data is an active area of research in medical imaging. Recent advances in quantum computing, particularly the integration of parameterized quantum circuits (PQCs) with classical machine learning architectures, offer new opportunities to develop models that may outperform traditional methods. However, quantum machine learning (QML) remains in its early stages and requires further experimental analysis to better understand its behavior and limitations. In this paper, we propose an end to end hybrid classical quantum convolutional neural network (CQ CNN) for AD detection using clinically formatted 3D MRI data. Our approach involves developing a framework to make 3D MRI data usable for machine learning, designing and training a brain tissue segmentation model (Skull Net), and training a diffusion model to generate synthetic images for the minority class. Our converged models exhibit potential quantum advantages, achieving higher accuracy in fewer epochs than classical models. The proposed beta8 3 qubit model achieves an accuracy of 97.50%, surpassing state of the art (SOTA) models while requiring significantly fewer computational resources. In particular, the architecture employs only 13K parameters (0.48 MB), reducing the parameter count by more than 99.99% compared to current SOTA models. Furthermore, the diffusion-generated data used to train our quantum models, in conjunction with real samples, preserve clinical structural standards, representing a notable first in the field of QML. We conclude that CQCNN architecture like models, with further improvements in gradient optimization techniques, could become a viable option and even a potential alternative to classical models for AD detection, especially in data limited and resource constrained clinical settings.

quant-ph↗

arXiv2025

Bridging Classical and Quantum Machine Learning: Knowledge Transfer From Classical to Quantum Neural Networks Using Knowledge Distillation

Quantum neural networks (QNNs), harnessing superposition and entanglement, have shown potential to surpass classical methods in complex learning tasks but remain limited by hardware constraints and noisy conditions. In this work, we present a novel framework for transferring knowledge from classical convolutional neural networks (CNNs) to QNNs via knowledge distillation, thereby reducing the need for resource intensive quantum training and error mitigation. We conduct extensive experiments using two parameterized quantum circuits (PQCs) with 4 and 8 qubits on MNIST, Fashion MNIST, and CIFAR10 datasets. The approach demonstrates consistent accuracy improvements attributed to distilled knowledge from larger classical networks. Through ablation studies, we systematically compare the effect of state of the art dimensionality reduction techniques fully connected layers, center cropping, principal component analysis, and pooling to compress high-dimensional image data prior to quantum encoding. Our findings reveal that fully connected layers retain the most salient features for QNN inference, thereby surpassing other down sampling approaches. Additionally, we examine state of the art data encoding methods (amplitude, angle, and qubit encoding) and identify amplitude encoding as the optimal strategy, yielding superior accuracy across all tested datasets and qubit configurations. Through computational analyses, we show that our distilled 4-qubit and 8-qubit QNNs achieve competitive performance while utilizing significantly fewer parameters than their classical counterparts. Our results establish a promising paradigm for bridging classical deep learning and emerging quantum computing, paving the way for more powerful, resource conscious models in quantum machine intelligence.

quant-ph↗

arXiv2024

Combining Entangled and Non-Entangled Based Quantum Key Distribution Protocol With GHZ State

This paper presents a novel hybrid Quantum Key Distribution ,QKD, protocol that combines entanglement based and non entanglement based approaches to optimize security and the number of generated keys. We introduce a dynamic system that integrates a three particle GHZ state method with the two state B92 protocol, using a quantum superposition state to probabilistically switch between them. The GHZ state component leverages strong three particle entanglement correlations for enhanced security, while the B92 component offers simplicity and potentially higher key generation rates. Implemented and simulated using Qiskit, our approach demonstrates higher number of generated keys compared to standalone protocols while maintaining robust security. We present a comprehensive analysis of the security properties and performance characteristics of the proposed protocol. The results show that this combined method effectively balances the trade offs inherent in QKD systems, offering a flexible framework adaptable to varying channel conditions and security requirements.This research contributes to ongoing efforts to make QKD more practical and efficient, potentially advancing the development of large scale, secured quantum networks.

quant-ph↗

arXiv2024

Multi-Layered Security System: Integrating Quantum Key Distribution with Classical Cryptography to Enhance Steganographic Security

In this paper, we present a novel cryptographic system that integrates Quantum Key Distribution (QKD) with classical encryption techniques to secure steganographic images. Our approach leverages the E91 QKD protocol to generate a shared secret key between communicating parties, ensuring the highest level of security against eavesdropping through the principles of quantum mechanics. This key is then hashed using the Secure Hash Algorithm (SHA) to provide a fixedlength, high-entropy key, which is subsequently utilized in symmetric encryption. We explore the use of AES (Advanced Encryption Standard) algorithms for encrypting steganographic images, which hide sensitive information within digital images to provide an additional layer of security through obscurity. The combination of QKD, hashing, and symmetric encryption offers a robust security framework that mitigates various attack vectors, enhancing the confidentiality and integrity of the transmitted data. Our experimental results demonstrate the feasibility and efficiency of the proposed system, highlighting its performance in terms of key generation rates, encryption/decryption speeds, and the computational overhead introduced by the hashing and steganographic processes. By integrating quantum and classical cryptographic methods with steganography, this work provides a comprehensive security solution that is highly resistant to both quantum and classical attacks, making it suitable for applications requiring stringent security measures. This paper contributes to the ongoing research in cryptographic systems, offering insights into the practical implementation and potential benefits of hybrid quantumclassical security protocols.

quant-ph↗

arXiv2024

Edge Detection Quantumized: A Novel Quantum Algorithm For Image Processing

Quantum image processing is a research field that explores the use of quantum computing and algorithms for image processing tasks such as image encoding and edge detection. Although classical edge detection algorithms perform reasonably well and are quite efficient, they become outright slower when it comes to large datasets with high-resolution images. Quantum computing promises to deliver a significant performance boost and breakthroughs in various sectors. Quantum Hadamard Edge Detection (QHED) algorithm, for example, works at constant time complexity, and thus detects edges much faster than any classical algorithm. However, the original QHED algorithm is designed for Quantum Probability Image Encoding (QPIE) and mainly works for binary images. This paper presents a novel protocol by combining the Flexible Representation of Quantum Images (FRQI) encoding and a modified QHED algorithm. An improved edge outline method has been proposed in this work resulting in a better object outline output and more accurate edge detection than the traditional QHED algorithm.

quant-ph↗

arXiv2023

Enhancing the security of image transmission in Quantum era: A Chaos-Assisted QKD Approach using entanglement

The emergence of quantum computing has introduced unprecedented security challenges to conventional cryptographic systems, particularly in the domain of optical communications. This research addresses these challenges by innovatively combining quantum key distribution (QKD), specifically the E91 protocol, with logistic chaotic maps to establish a secure image transmission scheme. Our approach utilizes the unpredictability of chaotic systems alongside the robust security mechanisms inherent in quantum entanglement. The scheme is further fortified with an eavesdropping detection mechanism based on CHSH inequality, thereby enhancing its resilience against unauthorized access. Through quantitative simulations, we demonstrate the effectiveness of this scheme in encrypting images, achieving high entropy and sensitivity to the original images. The results indicate a significant improvement in encryption and decryption efficiency, showcasing the potential of the scheme as a viable solution against the vulnerabilities posed by quantum computing advancements. Our research offers a novel perspective in secure optical communications, blending the principles of chaos theory with QKD to create a more robust cryptographic framework.

quant-ph↗

arXiv2023

Moving an Atom towards Right or Left Side by Applying Quantum Mechanical Matter Wave Near a Surface

The area of trapping the atoms or molecules using light has advanced tremendously in the last few decades. In contrast, the idea of controlling (not only trapping) the movement of atomic-sized particles using quantum mechanical matter waves is a completely new emerging area of particle manipulation. Though a single previous report has suggested the pulling of atoms based on matter wave tractor beams, an attempt is yet to be made to produce a lateral force (moving the atoms towards left or right) using this technique. This article demonstrates a matter wave-based manipulation scenario that gives rise to reversible lateral force on an atom due to the interaction energy of the quantum mechanical matter wave in the presence of a metal surface creating an asymmetrical set-up. For a symmetric set-up, no lateral force has been observed. We have performed several full wave simulations and analytical calculations on a particular set-up of Xenon scatterer atoms placed near a Copper surface, with two plane matter waves of Helium impinging in the direction parallel to the surface from two sides of the scatterer. By solving the time-independent Schrodinger equation and using the solution, quantum mechanical stress tensor formalism has been applied to compute the force acting on the particle. The full wave simulation results have been found in excellent agreement with the analytical calculations. The results for the adsorbed scatterer case suggests that our proposed technique can be an efficient cleaning procedure similar to electron-stimulated desorption for futuristic applications.

quant-ph↗

arXiv2023

Immense Fidelity Enhancement of Encoded Quantum Bell Pairs at Short and Long-distance Communication along with Generalized Design of Circuit

Quantum entanglement is a unique criterion of the quantum realm and an essential tool to secure quantum communication. Ensuring high-fidelity entanglement has always been a challenging task owing to interaction with the hostile channel environment created due to quantum noise and decoherence. Though several methods have been proposed, achieving almost 100% error correction is still a gigantic task. As one of the main contributions of this work, a new model for large distance communication has been introduced, which can correct all bit flip errors or other errors quite extensively if proper encoding is used. To achieve this purpose, at the very first step, the idea of differentiating the long and short-distance applications has been introduced. Short-distance is determined by the maximum range of applying unitary control gates by the qubit technology. As far as we know, there is no previous work that distinguishes long and short distance applications. At the beginning, we have applied stabilizer formalism and Repetition Code for decoding to distinguish the error correcting ability in long and short distance communication. Particularly for short distance communication, it has been demonstrated that a properly encoded bell state can identify all the bit flip, or phase flip errors with 100% accuracy theoretically. In contrast, if the bell states are used in long distance communication, the error-detecting and correcting ability reduces at huge amounts. To increase the fidelity significantly and correct the errors quite extensively for long-distance communication, a new model based on classical communication protocol has been proposed. All the required circuits in these processes have been generalized during encoding. Proposed analytical results have also been verified with the Simulation results of IBM QISKIT QASM.

quant-ph↗

arXiv2020

On the Modified Einstein-Laub and Modified Chu Optical Force Formulations

In several experiments involving material background, it has been observed that the Chu, Einstein-Laub and Ampere formulations of optical force lead to either different optical forces or wrong total optical force. In order to identify the exact reason behind such significant disagreements, we investigate the optical force in a number of tractor beam and lateral force experiments. We demonstrate that the modified Einstein-Laub or modified Chu formulations, obtained from two mathematical consistency conditions of force calculation, give the time-averaged force that agrees with the experiments. We consider both the chiral and achiral objects embedded in complex material backgrounds. Though the distinct formulations of optical force have been made mathematically equivalent in this work; the aspect of physical consistency of these distinct optical force formulations have also been investigated. It is known that the theory of Minkowski suggests zero bulk force inside a lossless object for which we still do not have any experimental verification. In contrast, both modified Einstein-Laub and modified Chu force formulations suggest non-zero bulk force inside a lossless object. Hence, for a future resolution of this discrepancy, we also suggest a possible experiment to investigate the bulk force and to check the validity of these distinct formulations.

physics.optics↗

arXiv2019

Improving Malaria Parasite Detection from Red Blood Cell using Deep Convolutional Neural Networks

Malaria is a female anopheles mosquito-bite inflicted life-threatening disease which is considered endemic in many parts of the world. This article focuses on improving malaria detection from patches segmented from microscopic images of red blood cell smears by introducing a deep convolutional neural network. Compared to the traditional methods that use tedious hand engineering feature extraction, the proposed method uses deep learning in an end-to-end arrangement that performs both feature extraction and classification directly from the raw segmented patches of the red blood smears. The dataset used in this study was taken from National Institute of Health named NIH Malaria Dataset. The evaluation metric accuracy and loss along with 5-fold cross validation was used to compare and select the best performing architecture. To maximize the performance, existing standard pre-processing techniques from the literature has also been experimented. In addition, several other complex architectures have been implemented and tested to pick the best performing model. A holdout test has also been conducted to verify how well the proposed model generalizes on unseen data. Our best model achieves an accuracy of almost 97.77%.

eess.IV↗

arXiv2019

Predicting and Forecasting the Price of Constituents and Index of Cryptocurrency Using Machine Learning

At present, cryptocurrencies have become a global phenomenon in financial sectors as it is one of the most traded financial instruments worldwide. Cryptocurrency is not only one of the most complicated and abstruse fields among financial instruments, but it is also deemed as a perplexing problem in finance due to its high volatility. This paper makes an attempt to apply machine learning techniques on the index and constituents of cryptocurrency with a goal to predict and forecast prices thereof. In particular, the purpose of this paper is to predict and forecast the close (closing) price of the cryptocurrency index 30 and nine constituents of cryptocurrencies using machine learning algorithms and models so that, it becomes easier for people to trade these currencies. We have used several machine learning techniques and algorithms and compared the models with each other to get the best output. We believe that our work will help reduce the challenges and difficulties faced by people, who invest in cryptocurrencies. Moreover, the obtained results can play a major role in cryptocurrency portfolio management and in observing the fluctuations in the prices of constituents of cryptocurrency market. We have also compared our approach with similar state of the art works from the literature, where machine learning approaches are considered for predicting and forecasting the prices of these currencies. In the sequel, we have found that our best approach presents better and competitive results than the best works from the literature thereby advancing the state of the art. Using such prediction and forecasting methods, people can easily understand the trend and it would be even easier for them to trade in a difficult and challenging financial instrument like cryptocurrency.

q-fin.ST↗

arXiv2018

Predicting the Stock Price of Frontier Markets Using Modified Black-Scholes Option Pricing Model and Machine Learning

The Black-Scholes Option pricing model (BSOPM) has long been in use for valuation of equity options to find the prices of stocks. In this work, using BSOPM, we have come up with a comparative analytical approach and numerical technique to find the price of call option and put option and considered these two prices as buying price and selling price of stocks of frontier markets so that we can predict the stock price (close price). Changes have been made to the model to find the parameters strike price and the time of expiration for calculating stock price of frontier markets. To verify the result obtained using modified BSOPM we have used machine learning approach using the software Rapidminer, where we have adopted different algorithms like the decision tree, ensemble learning method and neural network. It has been observed that, the prediction of close price using machine learning is very similar to the one obtained using BSOPM. Machine learning approach stands out to be a better predictor over BSOPM, because Black-Scholes-Merton equation includes risk and dividend parameter, which changes continuously. We have also numerically calculated volatility. As the prices of the stocks goes high due to overpricing, volatility increases at a tremendous rate and when volatility becomes very high market tends to fall, which can be observed and determined using our modified BSOPM. The proposed modified BSOPM has also been explained based on the analogy of Schrodinger equation (and heat equation) of quantum physics.

q-fin.ST↗

arXiv2018

Chiral standing waves and its trapping force on chiral particles

Up to now, in the literature of optical manipulation, optical force due to chirality usually coexists with the non-chiral force and the chiral force usually takes a very small portion of the total force. In this work, we investigate a case where the optical force exerted on an object is purely due to the chirality while there is zero force on non-chiral object. We find that a trapping force arises on chiral particles when it is placed in a field consisted of two orthogonally polarized counter-propagating plane waves. We have revealed the underlying physics of this force by modeling the particle as a chiral diploe and analytically study the optical force. We find besides chirality; the trapping force is also closely related to the dual electric-magnetic symmetry of field and dual asymmetry of material. We also demonstrate that the proposed idea is not restricted to dipolar chiral objects only. Chiral Mie objects can also be trapped based on the technique proposed in this article. Notably, such chiral trapping forces have been found robust by varying several parameters throughout the investigation. This trapping force may find applications in identifying object's chirality and the selective trapping of chiral objects.

physics.optics↗

arXiv2018

Determining the Increase or Decrease of Photon Momentum: Dielectric or Plasmonic Mie Object at Air-Liquid Interface

Though the interfacial tractor beam experiment supports the increase of photon momentum (i.e., Minkowski momentum), it is still a matter of investigation whether, inside matter, the photon momentum always increases. Considering the inhomogeneous or heterogeneous background, we have demonstrated that if the background and the half-immersed object are both non-absorbing, the transferred photon momentum to the object can be considered as the one of Minkowski exactly at the interface. In contrast, the presence of loss inside matter, either in the half-immersed object (i.e., plasmonic or lossy dielectric) or in the background, changes the whole situation. For such cases, our several demonstrations and proposed thought experiments have strongly supported the decrease of photon momentum instead of the usual perception of its increase. Although almost all the major radiation pressure experiments have so far supported the linear increase of photon momentum, our proposed simple experimental set-ups may introduce a novel way to observe and verify the exactly opposite proposal: the Abraham momentum of photon. Finally, as an interesting sidewalk, based on several parameters, a machine learning based system has been developed to predict the transferred momentum of photon within a very short time avoiding time-consuming full simulation.

physics.optics↗

arXiv2018

A General Way to Control the Reversal of Near Field Optical Binding Force between Plasmonic or Dielectric Dimers

Controlling the near field optical binding force can be a key factor for particle clustering, aggregation and localized surface plasmon sensors. So far there is no generic way to reverse the near field optical binding force for plasmonic or dielectric nano-dimers of distinct shapes (cube, cylinder, ring, sphere). In this article, for both plasmonic and dielectric objects, we have demonstrated a general way to control the reversal of near field binding force for different shaped dimer sets. The force reversal is achieved by simple breaking of symmetry, considering the nano particles are half or less than half immersed in an inhomogeneous dielectric background, i.e. at air-water interface. Such reversals have been explained based on Fano resonance, interference fields, unusual behavior of optical Lorentz force and image charge theory. However, if the dimer set is placed over a dielectric interface or fully inside a homogeneous medium, the sign of binding force does not reverse. Our proposed configuration provides a generic mechanism of archiving binding force reversal for both plasmonic and dielectric objects, which can be verified by very simple experimental set-up.

physics.optics↗

arXiv2017

Spherical Plasmonic Heterodimers: Reversal of Optical Binding Force as the Effect of Symmetry Breaking

The stimulating connection between the reversal of near field plasmonic binding force and the role of symmetry breaking has not been investigated in detail in literature. As both bonding and anti-bonding modes are present in the visible spectra of well-known spherical plasmonic heterodimer sets, binding force reversal is commonly believed to occur for all such heterodimers. But our work suggests a very different proposal. We demonstrate that for the symmetry broken heterodimer configurations: reversal of lateral (for on-axis heterodimers) and longitudinal (for off-axis heterodimers: end-fire and nearly end-fire configurations) near field binding force follow fully distinct mechanisms; i.e. later one on relative orientation and constructive dipole-quadrupole resonance but the former one on light polarization and the induced electric resonance. Interestingly, the reversal of longitudinal near field binding force can be easily controlled just by changing the direction of light propagation or just their relative orientation. Though it is commonly believed that plasmonic forces mostly arise from the surface force and Fano resonance can be a promising way to achieve binding force reversal; our study based on Lorentz force dynamics suggests notably opposite proposals (for both instances) for the case of plasmonic spherical heterodimers. Observations of this article can be very useful for improved sensors, particle clustering and aggregation.

physics.optics↗