Qubernetes: Towards a Unified Cloud-Native Execution Platform for Hybrid Classic-Quantum Computing
<|reference_start|>Qubernetes: Towards a Unified Cloud-Native Execution Platform for Hybrid Classic-Quantum Computing: Context: The emergence of quantum computing proposes a revolutionary paradigm that can radically transform numerous scientific and industrial application domains. The ability of quantum computers to scale computations beyond what the current computers are capable of implies better performance and efficiency for certain algorithmic tasks.   Objective: However, to benefit from such improvement, quantum computers must be integrated with existing software systems, a process that is not straightforward. In this paper, we propose a unified execution model that addresses the challenges that emerge from building hybrid classical-quantum applications at scale.   Method: Following the Design Science Research methodology, we proposed a convention for mapping quantum resources and artifacts to Kubernetes concepts. Then, in an experimental Kubernetes cluster, we conducted experiments for scheduling and executing quantum tasks on both quantum simulators and hardware.   Results: The experimental results demonstrate that the proposed platform Qubernetes (or Kubernetes for quantum) exposes the quantum computation tasks and hardware capabilities following established cloud-native principles, allowing seamless integration into the larger Kubernetes ecosystem.   Conclusion: The quantum computing potential cannot be realised without seamless integration into classical computing. By validating that it is practical to execute quantum tasks in a Kubernetes infrastructure, we pave the way for leveraging the existing Kubernetes ecosystem as an enabler for hybrid classical-quantum computing.<|reference_end|>
@article{stirbu2024qubernetes:,
    title={Qubernetes: Towards a Unified Cloud-Native Execution Platform for Hybrid
  Classic-Quantum Computing},
    author={Vlad Stirbu, Otso Kinanen, Majid Haghparast, Tommi Mikkonen},
    journal={arXiv preprint arXiv:2408.01436},
    year={2024},
    doi={10.1016/j.infsof.2024.107529},
    archivePrefix={arXiv},
    eprint={2408.01436},
    primaryClass={quant-ph cs.ET cs.SE}
}