Nowadays, interest is shown in the development of quantum computers because in the future it is expected to reach their potential computational power, which is superior to legacy computers. Since the day where quantum computers phase the current technology into obsolescence is still far out of reach, a period of coexistence between the two technologies is expected as these new devices are improved. One of the possible uses for quantum computers is in datacenters, offering services as cloud. Anticipating to their introduction in datacenters, this Bachelor’s Thesis goal has been to develop a system that orchestrates and manages petitions for a datacenter with hybrid resources, with the objective of making optimal use of the two available technologies.
Nowadays, interest is shown in the development of quantum computers because in the future it is expected to reach their potential computational power, which is superior to legacy computers. Since the day where quantum computers phase the current technology into obsolescence is still far out of reach, a period of coexistence between the two technologies is expected as these new devices are improved. One of the possible uses for quantum computers is in datacenters, offering services as cloud. Anticipating to their introduction in datacenters, this Bachelor’s Thesis goal has been to develop a system that orchestrates and manages petitions for a datacenter with hybrid resources, with the objective of making optimal use of the two available technologies.
Hybrid quantum datacentres are advancing rapidly, and yet most of the efforts are pointing to hardware development, while the software layer that orchestrates classical and quantum resources remains underdeveloped. This thesis designs and implements a state aware hybrid orchestrator that receives workload requests and places them into the designated cluster. The orchestrator is built as a set of REST services organised into NorthBound, allocation, resource and Southbound blocks. The classical cluster is deployed on an OpenStack environment using DevStack, while the quantum cluster is emulated by quantum agents. Two placement algorithms are implemented, First Fit and Load Balancing. Both operate over both clusters, they filter and select the corresponding node that suits the logic of the algorithm. Every allocation is persisted so the system recovers the status after a restart. A natural language entry layer was also implemented, combining retrieval augmentation generation with a large language model, converting the entry into a validated typed schema and routing to the appropriate tier, while a dashboard exposes the full pipeline and the live infrastructure state with role based visibility. The system was evaluated under identical 100 request campaigns using a mock backend that enables large scale testing without saturating the physical laboratory. Load Balancing increased the number of successful allocations by 19.15 per cent over First Fit, an advantage concentrated in the RAM constrained classical tier, and the natural language layer reached a routing accuracy of 96.8 per cent. The results confirm that a natural language interface can drive an orchestrator reliably. The main limitations are the emulated quantum cluster and a single classical laboratory deployment, future work includes integrating a real quantum backend, adding authentication to the dashboard, and introducing time aware resource release.
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 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.
As the demand for high-performance and dynam-ically adaptable network services continues to grow, the convergence of quantum computing and network orchestration represents a promising development. This paper explores the transformative potential of quantum computing in the orches-tration of network services and introduces new paradigms that transcend classical limitations. Using a case study, we propose a workflow for an exemplary link failure process for generative AI (GenAI) and quantum computing-based service orchestration in an edge cloud (EC) network. Later, we address the challenges of integrating services based on quantum computing (using a quan-tum search algorithm to select the best connections) with GenAI-based topology control and management in an orchestration framework. Our work also introduces multi-solution quantum search for orchestrating services across different edge clouds, emphasizing efficiency and accuracy. Algorithmic considerations, including the impact of the oracle and hybrid approaches, are also thoroughly explored. We conclude the paper with potentials and comparisons of possible GenAl techniques and quantum search algorithms in terms of their advantages and disadvantages.
What else than Hybrid Quantum Computing will dominate data centers, just within a decade? But until now, it is unclear how precisely such high-performance computers will be constructed, even the fundamental functionality is in question. Thus, it is very hard to prepare today, from a programmer perspective, in-depth for the anticipated near-term programming paradigm shift due to quantum computers. Furthermore, the industry is reluctant to invest heavily into quantum software development right now, since under this uncertainty regarding the future development of hardware and Cloud services, no one would like to see all these efforts to be ruined by a novel technology, an unforeseen new machine, which would eradicate the software in place, which the industry had developed so costly. This calls for a more generic view on Hybrid Quantum Computing, on one hand hardware agnostic, but still anticipating the fundamental laws of nature which rule any future quantum computing system, regardless of its engineered excellence. This generalization can be done with the introduction of a Virtual Quantum Processor, a piece of imaginary hardware, which is described detailed enough, to emulate a generic hybrid quantum machine based on a set of instructions within a Turing machine. If this could be accomplished, we would retrieve a Uniform Computing Model for Hybrid Quantum Software, which can be applied later to any physical representation of quantum computing hardware, but would run already today on our current machines.
Quantum computing has emerged as a transformative technology capable of solving complex computational problems beyond the capabilities of classical systems. However, severe hardware limitations, qubit decoherence, and high implementation costs prevent it from replacing classical cloud infrastructure. Instead, cloud and quantum computing are evolving as complementary technologies. Through Quantum-as-a-Service (QaaS) and hybrid cloud–quantum architectures, cloud platforms provide scalable infrastructure, data management, and resource orchestration, while quantum processors accelerate specialized computational tasks. This review systematically examines the relationship between cloud computing and quantum computing by analyzing their technical differences, capabilities, application areas, current challenges, limitations, and future research directions. The findings indicate that cloud computing will continue to serve as the backbone of modern digital infrastructure, while quantum computing will enhance its capabilities for solving complex computational problems. Together, these technologies are expected to drive the next generation of intelligent, scalable, and high-performance computing systems. Keywords: cloud computing; quantum computing; Quantum as a Service (QaaS); Hybrid Cloud–Quantum Architecture; Post-Quantum Security; QMLOps