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Paving the Way to Hybrid Quantum-Classical Scientific Workflows

Sandeep Suresh Cranganore, Vincenzo De Maio, Ivona Brandic, Ewa Deelman

TL;DR

This work tackles the problem of accelerating scientific workflows by integrating near-term quantum devices into the HPC continuum. It formalizes hybrid quantum-classical workflows, identifies quantum candidate tasks within a molecular dynamics use case, and demonstrates how to map classic tasks to quantum equivalents under hardware constraints. A software-architecture blueprint for a Hybrid Workflow Management System (WMS) is proposed, detailing components such as hardware descriptors, quantum task repositories, and data/control-flow models, along with methods for cost-based optimization and data-driven hyperparameter tuning. The paper also discusses major challenges—hardware heterogeneity, performance modeling, error mitigation, and integration—and outlines a practical outlook toward broadening quantum-accelerated scientific computing across domains.

Abstract

The increasing growth of data volume, and the consequent explosion in demand for computational power, are affecting scientific computing, as shown by the rise of extreme data scientific workflows. As the need for computing power increases, quantum computing has been proposed as a way to deliver it. It may provide significant theoretical speedups for many scientific applications (i.e., molecular dynamics, quantum chemistry, combinatorial optimization, and machine learning). Therefore, integrating quantum computers into the computing continuum constitutes a promising way to speed up scientific computation. However, the scientific computing community still lacks the necessary tools and expertise to fully harness the power of quantum computers in the execution of complex applications such as scientific workflows. In this work, we describe the main characteristics of quantum computing and its main benefits for scientific applications, then we formalize hybrid quantum-classic workflows, explore how to identify quantum components and map them onto resources. We demonstrate concepts on a real use case and define a software architecture for a hybrid workflow management system.

Paving the Way to Hybrid Quantum-Classical Scientific Workflows

TL;DR

This work tackles the problem of accelerating scientific workflows by integrating near-term quantum devices into the HPC continuum. It formalizes hybrid quantum-classical workflows, identifies quantum candidate tasks within a molecular dynamics use case, and demonstrates how to map classic tasks to quantum equivalents under hardware constraints. A software-architecture blueprint for a Hybrid Workflow Management System (WMS) is proposed, detailing components such as hardware descriptors, quantum task repositories, and data/control-flow models, along with methods for cost-based optimization and data-driven hyperparameter tuning. The paper also discusses major challenges—hardware heterogeneity, performance modeling, error mitigation, and integration—and outlines a practical outlook toward broadening quantum-accelerated scientific computing across domains.

Abstract

The increasing growth of data volume, and the consequent explosion in demand for computational power, are affecting scientific computing, as shown by the rise of extreme data scientific workflows. As the need for computing power increases, quantum computing has been proposed as a way to deliver it. It may provide significant theoretical speedups for many scientific applications (i.e., molecular dynamics, quantum chemistry, combinatorial optimization, and machine learning). Therefore, integrating quantum computers into the computing continuum constitutes a promising way to speed up scientific computation. However, the scientific computing community still lacks the necessary tools and expertise to fully harness the power of quantum computers in the execution of complex applications such as scientific workflows. In this work, we describe the main characteristics of quantum computing and its main benefits for scientific applications, then we formalize hybrid quantum-classic workflows, explore how to identify quantum components and map them onto resources. We demonstrate concepts on a real use case and define a software architecture for a hybrid workflow management system.
Paper Structure (48 sections, 2 theorems, 61 equations, 10 figures, 1 algorithm)

This paper contains 48 sections, 2 theorems, 61 equations, 10 figures, 1 algorithm.

Table of Contents

  1. Introduction
  2. Related Work
  3. Background
  4. Scientific Workflows
  5. Workflow Management Systems
  6. Quantum Computing
  7. Hybrid Quantum-Classic Systems
  8. From Classic to Hybrid Workflows
  9. Hybrid Workflows
  10. A Molecular Dynamics Use Case
  11. Identification of Quantum candidates
  12. Mapping from Classic to Hybrid Workflow
  13. Cost Function Optimization
  14. Data-Driven Hyperparameters Selection
  15. Generalizing Hybrid WMSs
  16. ...and 33 more sections

Key Result

Theorem 1

Schmidt decomposition: Let {$\mathbb{H}_{1} \mathbb{H}_{2}, ..., \mathbb{H}_{n}$} be Hilbert spaces of dimensions $p_1, p_2, ..., p_n$ respectively. Assume that $p_n \geq p_{n-1} \geq.... \geq p_1$. For any state in this composite (multi-partite) system, i.e, $\ket{\xi}\in \mathbb{H}_{1} \otimes \m Where, $\lambda_i$ are the Schmidt coefficients, and $|\phi_l\rangle$, ...., $|\psi_l\rangle$ are t

Figures (10)

  • Figure 1: Schematics of hybrid quantum-classical systems.
  • Figure 2: Example of Transformation into Hybrid Workflow
  • Figure 3: Types of Quantum Tasks.
  • Figure 4: Hybrid Quantum-Classical MD Simulation.
  • Figure 5: Quantum integrated MD workflows for distance matrix computations.
  • ...and 5 more figures

Theorems & Definitions (8)

  • Definition 3.1: Scientific Workflows
  • Definition 4.1: Hybrid Workflows
  • Definition A.1: Hermitian Matrices/Operators
  • Definition A.2: Unitary Matrices/Operators
  • Theorem 1
  • Definition C.1
  • Theorem 2
  • proof