Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Towards Quantum-Enhanced Cloud Platforms: Bridging Classical and Quantum Computing for Future Workloads

Lakshminarayana Reddy Kothapalli Sondinti

Published Jan 1, 2025• 0 citations
Publisher
DOI
10.2139/ssrn.5058975

Related papers

Towards Quantum-Enhanced Cloud Platforms: Bridging Classical and Quantum Computing for Future Workloads

Lakshminarayana Reddy Kothapalli Sondinti

•Jan 1, 2025•0 citations

Towards Quantum-Enhanced Cloud Platforms: Bridging Classical and Quantum Computing for Future Workloads

Lakshminarayana Reddy Kothapalli Sondinti, Srinivas Kalisetty, Tulasi Naga Subhash Polineni +1 more

The rapid advancement of quantum computing technology presents an opportunity to revolutionize cloud computing platforms, enabling the execution of complex workloads that are beyond the reach of classical systems. This paper explores the potential of quantum-enhanced cloud platforms, focusing on bridging classical and quantum computing to support future workloads. We examine the integration of quantum processors with classical cloud infrastructure, highlighting the challenges and benefits of hybrid architectures that combine the strengths of both paradigms. Key topics include quantum resource management, quantum programming models, and the development of algorithms that leverage quantum speedup for optimization, machine learning, and data analysis. Additionally, we address the scalability, security, and interoperability concerns that must be overcome for effective deployment in real-world cloud environments. By offering insights into the convergence of classical and quantum computing, this paper provides a roadmap for the evolution of cloud platforms capable of supporting next-generation applications and workloads.

Journal for ReAttach Therapy and Developmental Diversities•Jan 1, 2023•39 citations

Towards quantum cloud computing

Xiaogang Qiang, Xiao Qi Zhou, Kanin Aungskunsiri +1 more

European Quantum Electronics Conference (EQEC)•Jul 21, 2014•0 citations

QUANTUM COMPUTING MEETS CLOUD: INTEGRATING AND ENHANCING HIGHPERFORMANCE COMPUTING APPLICATIONS

National Press Associates

•Jan 19, 2026•0 citations

Q-OS: A System-Level Operating Framework for Quantum-Enhanced AI Workload Management in Future Computing

Mahesh Babu Chittem, Ashok Kumar Pradhan

The convergence of Quantum Computing (QC) and Artificial Intelligence (AI) is reshaping computational paradigms, creating the need for a new class of system software capable of managing hybrid workloads. This paper presents Q-OS, a Quantum-Aware Operating System designed to orchestrate hybrid quantum-classical AI pipelines across cloud and edge environments. Q-OS introduces three novel system components: a Quantum Scheduling Kernel that intelligently allocates portions of AI workloads (such as optimization or sampling) to quantum co-processors; a Quantum Resource Virtualization Layer that abstracts quantum devices as logical accelerators; and a Quantum-AI Compiler Interface that dynamically maps AI operations from frameworks such as TensorFlow and PyTorch into executable quantum kernels. Experimental evaluations using hybrid workloads-including large language model inference (The Pile dataset), quantum-classical generative adversarial networks (CelebA dataset), and reinforcement learning control (OpenAI Gym)-demonstrate that Q-OS achieves up to 32% reduction in end-to-end latency and 27 % improvement in energy efficiency compared to classical distributed systems. The results indicate that Q-OS establishes a scalable foundation for the next generation of intelligent computing infrastructures, bridging quantum and AI ecosystems for real-world deployment.

International Conference on Emerging Systems and Intelligent Computing (ESIC)•Feb 7, 2026•0 citations

Quantum-Inspired Optimization of Cloud Infrastructure for Reliability and Cost Efficiency

Naga Sai Bandhavi Sakhamuri

Quantum-inspired optimization emerges as a transformative paradigm for cloud infrastructure management, addressing the increasing complexity and multi-dimensional challenges faced by modern distributed systems. This article introduces a comprehensive framework that applies quantum computational principles to classical infrastructure, enabling more efficient navigation of complex solution landscapes without requiring quantum hardware. The framework targets critical operational areas including workload distribution, auto-scaling, resource allocation, and fault tolerance enhancement. By leveraging quantum-inspired algorithmic approaches such as quantum annealing simulations and hybrid methods, the solution demonstrates significant advantages over conventional optimization techniques, particularly as infrastructure complexity increases. The implementation incorporates seamless integration with existing cloud platforms through non-invasive APIs and abstraction layers. Experimental results reveal improved resource utilization, enhanced reliability during failure scenarios, substantial cost reductions, and favorable scalability characteristics. The quantum-inspired approach offers a practical pathway toward addressing the exponentially growing complexity of cloud infrastructure optimization while providing immediate benefits using classical computing resources and establishing foundations for future integration with quantum computing services.

European Journal of Computer Science and Information Technology•May 15, 2025•1 citation
The rapid advancement of quantum computing technology presents an opportunity to revolutionize cloud computing platforms, enabling the execution of complex workloads that are beyond the reach of classical systems. This paper explores the potential of quantum-enhanced cloud platforms, focusing on bridging classical and quantum computing to support future workloads. We examine the integration of quantum processors with classical cloud infrastructure, highlighting the challenges and benefits of hybrid architectures that combine the strengths of both paradigms. Key topics include quantum resource management, quantum programming models, and the development of algorithms that leverage quantum speedup for optimization, machine learning, and data analysis. Additionally, we address the scalability, security, and interoperability concerns that must be overcome for effective deployment in real-world cloud environments. By offering insights into the convergence of classical and quantum computing, this paper provides a roadmap for the evolution of cloud platforms capable of supporting next-generation applications and workloads.
The convergence of Quantum Computing (QC) and Artificial Intelligence (AI) is reshaping computational paradigms, creating the need for a new class of system software capable of managing hybrid workloads. This paper presents Q-OS, a Quantum-Aware Operating System designed to orchestrate hybrid quantum-classical AI pipelines across cloud and edge environments. Q-OS introduces three novel system components: a Quantum Scheduling Kernel that intelligently allocates portions of AI workloads (such as optimization or sampling) to quantum co-processors; a Quantum Resource Virtualization Layer that abstracts quantum devices as logical accelerators; and a Quantum-AI Compiler Interface that dynamically maps AI operations from frameworks such as TensorFlow and PyTorch into executable quantum kernels. Experimental evaluations using hybrid workloads-including large language model inference (The Pile dataset), quantum-classical generative adversarial networks (CelebA dataset), and reinforcement learning control (OpenAI Gym)-demonstrate that Q-OS achieves up to 32% reduction in end-to-end latency and 27 % improvement in energy efficiency compared to classical distributed systems. The results indicate that Q-OS establishes a scalable foundation for the next generation of intelligent computing infrastructures, bridging quantum and AI ecosystems for real-world deployment.
Quantum-inspired optimization emerges as a transformative paradigm for cloud infrastructure management, addressing the increasing complexity and multi-dimensional challenges faced by modern distributed systems. This article introduces a comprehensive framework that applies quantum computational principles to classical infrastructure, enabling more efficient navigation of complex solution landscapes without requiring quantum hardware. The framework targets critical operational areas including workload distribution, auto-scaling, resource allocation, and fault tolerance enhancement. By leveraging quantum-inspired algorithmic approaches such as quantum annealing simulations and hybrid methods, the solution demonstrates significant advantages over conventional optimization techniques, particularly as infrastructure complexity increases. The implementation incorporates seamless integration with existing cloud platforms through non-invasive APIs and abstraction layers. Experimental results reveal improved resource utilization, enhanced reliability during failure scenarios, substantial cost reductions, and favorable scalability characteristics. The quantum-inspired approach offers a practical pathway toward addressing the exponentially growing complexity of cloud infrastructure optimization while providing immediate benefits using classical computing resources and establishing foundations for future integration with quantum computing services.