Modern cybersecurity faces unprecedented challenges from sophisticated malware attacks targeting critical infrastructure systems, necessitating revolutionary detection methodologies that transcend the limitations of classical approaches. Traditional signature-based detection methods prove inadequate against zero-day threats, while classical machine learning approaches struggle with computational complexity and real-time adaptability to evolving attack patterns. This research proposes an advanced malware detection framework leveraging Quantum Convolutional Neural Networks (QCNNs) through a novel multi-encoding distributed architecture specifically designed to address current quantum hardware constraints while maximizing quantum computational advantages. The methodology employs a comprehensive six-stage pipeline integrating quantum data encoding strategies including hybrid angle encoding with section-specific Portable Executable (PE) binary analysis using distributed 8-qubit QCNNs. Each malware binary is systematically decomposed into critical PE sections (.text, .data, .rdata, .rsrc, .reloc), converted to 8×8 grayscale images, and processed through specialized quantum circuits employing parameterized gates with entanglement patterns for enhanced feature extraction. The distributed quantum processing outputs are then integrated through classical ensemble methods including XGBoost and Random Forest for final classification, with this hybrid classical-quantum integration serving as an optional enhancement to the core quantum framework. Experimental validation will be conducted on comprehensive malware datasets including BODMAS and PEMachineLearning repositories, along with additional specialized datasets, to ensure robust evaluation across diverse attack vectors. The proposed framework aims to significantly outperform classical approaches while maintaining practical deployment feasibility on NISQ-era quantum devices. Expected contributions include establishing systematic benchmarking standards for quantum cybersecurity applications, developing scalable quantum-classical hybrid integration strategies, and creating open-source frameworks for quantum-enhanced malware detection. This research addresses critical gaps in current quantum machine learning applications for cybersecurity, providing both theoretical advances in distributed quantum processing and practical solutions for real-world malware detection challenges in an increasingly connected digital infrastructure.

Keywords: quantum machine learning, quantum convolutional neural networks, malware detection, quantum computing, cybersecurity, distributed computing, hybrid quantum-classical systems, NISQ devices

This repository implements a novel, distributed QCNN architecture that analyzes malware binaries by splitting them into critical PE sections, encoding each for quantum processing, and classifying them using both quantum and classical ensemble methods. The design is optimized for NISQ-era quantum devices.

• Distributed QCNNs:
  Five independent 8-qubit QCNNs, each specialized for a PE section (.text, .data, .rdata, .rsrc, .reloc).

• Quantum Data Encoding:
  Supports amplitude, qubit, dense qubit, hybrid direct, and hybrid angle encoding.

• Hybrid Integration:
  Combines quantum outputs with classical models (XGBoost, Random Forest, neural networks).

• NISQ-Ready:
  Resource-efficient, modular design for current quantum hardware.

• Comprehensive Benchmarking:
  Evaluates accuracy, resource use, robustness, and hardware compatibility.