arXiv subjects

Subham Das

Publications and source records attributed to Subham Das.

3 recordsLinked to original sources

arXiv2026

Cat-Code-Protected Controlled Quantum Communication via Non-Local CNOT Gates over Star Quantum Networks

Controlled quantum communication enables secure state transfer between a sender and receiver with the assistance of one or more controllers. However, practical implementation over optical fibre networks is severely hindered by amplitude damping, which reduces fidelity exponentially with distance. We address this challenge by combining two powerful techniques: cat-state encoding for error correction and optimal non-local CNOT gates for distributed gate implementation. The protocol eliminates the need for the physical position qubit itself to travel through the optical fibre, reducing damping events. We show, through density-matrix simulations, that our cat-code-protected protocol with a non-local CNOT operation achieves higher fidelity at 50 km, significantly outperforming the standard protocol. We analyse the protocol's security against beam-splitter attacks and show that CHSH tests provide security against beam-splitter attacks on the distributed entanglement resource despite the cat code's error correction. Our results establish that cat-code-protected controlled quantum communication is feasible with current technology and structurally extensible to multiple controllers, providing a theoretical framework for studying error-corrected controlled quantum communication over long-distance star quantum networks.

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

QDCNN: Quantum Deep Learning for Enhancing Safety and Reliability in Autonomous Transportation Systems

In transportation cyber-physical systems (CPS), ensuring safety and reliability in real-time decision-making is essential for successfully deploying autonomous vehicles and intelligent transportation networks. However, these systems face significant challenges, such as computational complexity and the ability to handle ambiguous inputs like shadows in complex environments. This paper introduces a Quantum Deep Convolutional Neural Network (QDCNN) designed to enhance the safety and reliability of CPS in transportation by leveraging quantum algorithms. At the core of QDCNN is the UU{\dag} method, which is utilized to improve shadow detection through a propagation algorithm that trains the centroid value with preprocessing and postprocessing operations to classify shadow regions in images accurately. The proposed QDCNN is evaluated on three datasets on normal conditions and one road affected by rain to test its robustness. It outperforms existing methods in terms of computational efficiency, achieving a shadow detection time of just 0.0049352 seconds, faster than classical algorithms like intensity-based thresholding (0.03 seconds), chromaticity-based shadow detection (1.47 seconds), and local binary pattern techniques (2.05 seconds). This remarkable speed, superior accuracy, and noise resilience demonstrate the key factors for safe navigation in autonomous transportation in real-time. This research demonstrates the potential of quantum-enhanced models in addressing critical limitations of classical methods, contributing to more dependable and robust autonomous transportation systems within the CPS framework.

quant-ph↗