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International Conference on Intelligent Multimedia, Networking, and Security (IMNS)

Orchestrating High-Performance Quantum Applications in Distributed Environments

Alejandro Olvera, Harry Fu, Song Fu

Published Aug 1, 2026• 0 citations
Publisher
DOI
10.1109/imns67862.2026.11655311

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In the rapidly evolving domain of smart healthcare, the integration of Docker containers and Kubernetes with Internet of Things (IoT) edge cloud orchestration has significantly enhanced the performance, scalability, and modularity of healthcare applications. However, despite the efficiency benefits provided by microservices architecture, substantial security vulnerabilities persist, particularly in the face of the threat of quantum computing. Quantum algorithms, such as Shor’s and Grover’s, pose a significant risk to conventional encryption schemes, potentially compromising the integrity and confidentiality of healthcare data distributed across edge cloud environments. To address these critical concerns, we propose AQ-ResCon: an Adaptive Quantum-Resistant Lattice-Based Key Agreement Protocol designed for secure and resilient distributed container orchestration. AQ-ResCon leverages the hardness of the Learning With Errors (LWE) problem in lattice-based cryptography to provide a robust defence against quantum attacks. We further introduce the AQ-ResCon Scheduler Algorithm, which efficiently manages and executes healthcare workloads across decentralised IoT-edge-cloud nodes, ensuring secure data flow and orchestration. Extensive testbed experiments were conducted to evaluate the performance and security effectiveness of AQ-ResCon under realistic containerised healthcare scenarios. Results demonstrate that AQ-ResCon achieves up to 36% improvement in scheduling efficiency, 41% reduction in key compromise rates, and 28% lower latency compared to existing quantum-vulnerable orchestration protocols. Additionally, the AQ-ResCon protocol maintained consistent performance under simulated quantum attack conditions, validating its adaptability and resilience. These outcomes confirm that AQ-ResCon is a viable, secure, and future-proof solution for safeguarding microservices-based healthcare applications against evolving quantum threats in edge cloud environments.
The convergence of quantum computing and high-performance computing (HPC) is giving rise to hybrid cloud platforms that coordinate workflows across classical and quantum resources. These heterogeneous environments introduce new challenges for workload management, including device variability, noise-prone quantum processors, and the need for synchronized orchestration across distributed systems. This survey reviews state-of-the-art techniques for scheduling, resource allocation, and workflow management in classical HPC, quantum clouds, and emerging hybrid infrastructures. A taxonomy is proposed to categorize workload management approaches by scheduling strategy, allocation model, orchestration mechanism, and performance metric. We present HybridCloudSim, a novel simulation framework designed to model hybrid quantum–HPC cloud environments and optimize resource usage under realistic constraints. HybridCloudSim is a conceptual foundation and a practical tool for advancing workload management in hybrid quantum–HPC systems.
The convergence of quantum computing, neuromorphic learning, and distributed cloud infrastructures has occurred very rapidly, and intelligent systems are now providing new opportunities, but the challenge of instability, complexity of orchestration, and noise sensitivity remains in the way of practical integration. The proposed work is based on a hybrid quantum and neuromorphic architecture, which is the integration of event-based neuromorphic adaptation and quantum-assisted global optimization, orchestrated by cloud-HPC. The architecture presents the thermodynamically regularized learning and resourceful task scheduling to the probabilistic search and the continuous local adaptation. Experimental evaluation across financial modeling, medical imaging, and physical system prediction shows an average accuracy of 94.3%, a reduction in convergence time to 3.5 s, and improved reconstruction quality (SSIM = 0.93, PSNR = 33.0 dB). Workflow robustness also increases, achieving a 96.1% task success rate under secure orchestration.