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Improving Quantum Developer Experience with Kubernetes and Jupyter Notebooks

Otso Kinanen, Andrés D. Muñoz-Moller, Vlad Stirbu, Tommi Mikkonen

TL;DR

The paper tackles the challenge that quantum software development is hindered by scarce, expensive hardware and complex toolchains. It introduces Qubernetes, a custom Jupyter kernel that packages notebook cells as Kubernetes Jobs and executes them on GPU-accelerated remote clusters, enabling seamless local-remote switching. Key contributions include the kernel architecture, the task execution model, and an empirical evaluation across hardware configurations demonstrating speedups, ease of use, and cost savings. The approach has practical impact by enabling scalable, accessible quantum development workflows that leverage cloud-native HPC resources for larger qubit simulations without exposing developers to cluster management.

Abstract

Quantum computing proposes a revolutionary paradigm that can radically transform numerous scientific and industrial application domains. To realize this promise, new capabilities need software solutions that are able to effectively harness its power. However, developers face significant challenges when developing quantum software due to the high computational demands of simulating quantum computers on classical systems. In this paper, we investigate the potential of using an accessible and cost-efficient manner remote computational capabilities to improve the experience of quantum software developers.

Improving Quantum Developer Experience with Kubernetes and Jupyter Notebooks

TL;DR

The paper tackles the challenge that quantum software development is hindered by scarce, expensive hardware and complex toolchains. It introduces Qubernetes, a custom Jupyter kernel that packages notebook cells as Kubernetes Jobs and executes them on GPU-accelerated remote clusters, enabling seamless local-remote switching. Key contributions include the kernel architecture, the task execution model, and an empirical evaluation across hardware configurations demonstrating speedups, ease of use, and cost savings. The approach has practical impact by enabling scalable, accessible quantum development workflows that leverage cloud-native HPC resources for larger qubit simulations without exposing developers to cluster management.

Abstract

Quantum computing proposes a revolutionary paradigm that can radically transform numerous scientific and industrial application domains. To realize this promise, new capabilities need software solutions that are able to effectively harness its power. However, developers face significant challenges when developing quantum software due to the high computational demands of simulating quantum computers on classical systems. In this paper, we investigate the potential of using an accessible and cost-efficient manner remote computational capabilities to improve the experience of quantum software developers.
Paper Structure (11 sections, 7 figures, 1 table)

This paper contains 11 sections, 7 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background and motivation
  3. Software development life cycle
  4. Quantum development toolkits and simulators
  5. Notebooks
  6. Computing at-scale paradigms
  7. The Jupyter kernel for Qubernetes
  8. System architecture and components
  9. Task execution model
  10. Discussion
  11. Conclusion and future work

Figures (7)

  • Figure 1: Quantum software development life cycle and areas where developers and operators interact with quantum hardware or simulators: (1) the design and development of quantum algorithms, and (2) scheduling and executing the computation on an available and capable quantum computer
  • Figure 2: A layered view at the Qiskit software stack
  • Figure 3: JupyterHub on Kubernetes architecture
  • Figure 4: System architecture
  • Figure 5: Executing a notebook cell on Kubernetes cluster using the q8s_kernel
  • ...and 2 more figures