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Quantum Computing Enhanced Service Ecosystem for Simulation in Manufacturing

Wolfgang Maass, Ankit Agrawal, Alessandro Ciani, Sven Danz, Alejandro Delgadillo, Philipp Ganser, Pascal Kienast, Marco Kulig, Valentina König, Nil Rodellas-Gràcia, Rivan Rughubar, Stefan Schröder, Marc Stautner, Hannah Stein, Tobias Stollenwerk, Daniel Zeuch, Frank K. Wilhelm

TL;DR

The paper addresses the challenge of accelerating large-scale manufacturing simulations by leveraging quantum computing and quantum-assisted learning within the QUASIM framework. It proposes a multi-layer Quantum-as-a-Service ecosystem that integrates QC into Industry 4.0 digital twins and demonstrates two industrially valuable use cases: milling dynamics and laser cutting, including both quantum-accelerated linear-algebra solvers and quantum-enhanced graph-based learning. Key contributions include a state-of-the-art synthesis of QC/QML primitives for classical simulations, an end-to-end perspective on eigenvalue problems such as $(K - \omega_i^2 M)\Phi_i=0$, and a scalable, hardware-agnostic local PQC-GNN approach for graph-structured manufacturing data. The work highlights the potential for meaningful speedups and reduced resource requirements, enabling near real-time decision making and more efficient high-value manufacturing processes through QC-enabled simulation and learning.

Abstract

Quantum computing (QC) and machine learning (ML), taken individually or combined into quantum-assisted ML (QML), are ascending computing paradigms whose calculations come with huge potential for speedup, increase in precision, and resource reductions. Likely improvements for numerical simulations in engineering imply the possibility of a strong economic impact on the manufacturing industry. In this project report, we propose a framework for a quantum computing-enhanced service ecosystem for simulation in manufacturing, consisting of various layers ranging from hardware to algorithms to service and organizational layers. In addition, we give insight into the current state of the art of applications research based on QC and QML, both from a scientific and an industrial point of view. We further analyse two high-value use cases with the aim of a quantitative evaluation of these new computing paradigms for industrially-relevant settings.

Quantum Computing Enhanced Service Ecosystem for Simulation in Manufacturing

TL;DR

The paper addresses the challenge of accelerating large-scale manufacturing simulations by leveraging quantum computing and quantum-assisted learning within the QUASIM framework. It proposes a multi-layer Quantum-as-a-Service ecosystem that integrates QC into Industry 4.0 digital twins and demonstrates two industrially valuable use cases: milling dynamics and laser cutting, including both quantum-accelerated linear-algebra solvers and quantum-enhanced graph-based learning. Key contributions include a state-of-the-art synthesis of QC/QML primitives for classical simulations, an end-to-end perspective on eigenvalue problems such as , and a scalable, hardware-agnostic local PQC-GNN approach for graph-structured manufacturing data. The work highlights the potential for meaningful speedups and reduced resource requirements, enabling near real-time decision making and more efficient high-value manufacturing processes through QC-enabled simulation and learning.

Abstract

Quantum computing (QC) and machine learning (ML), taken individually or combined into quantum-assisted ML (QML), are ascending computing paradigms whose calculations come with huge potential for speedup, increase in precision, and resource reductions. Likely improvements for numerical simulations in engineering imply the possibility of a strong economic impact on the manufacturing industry. In this project report, we propose a framework for a quantum computing-enhanced service ecosystem for simulation in manufacturing, consisting of various layers ranging from hardware to algorithms to service and organizational layers. In addition, we give insight into the current state of the art of applications research based on QC and QML, both from a scientific and an industrial point of view. We further analyse two high-value use cases with the aim of a quantitative evaluation of these new computing paradigms for industrially-relevant settings.
Paper Structure (11 sections, 4 equations, 3 figures)

This paper contains 11 sections, 4 equations, 3 figures.

Table of Contents

  1. Introduction
  2. Framework for QC-enhanced service ecosystem for simulation in manufacturing
  3. Quantum computing for classical simulation
  4. State of the art
  5. General approach in QUASIM
  6. Specific application: milling dynamics simulation
  7. Quantum machine learning for classical simulation
  8. State of the art
  9. General approach in QUASIM
  10. Specific application: laser cutting
  11. Summary and Outlook

Figures (3)

  • Figure 1: Envisioned QC-enhanced service ecosystem for simulation in manufacturing.
  • Figure 2: Simulated milling tool engagement. Shown are an in-process workpiece (IPW) of a single blade demonstrator (left), and the detailed representation of the chip removal (center) with the three characteristic chip parameters: length $l_\text{sp}$, thickness $h_\text{sp}$, and width $b_\text{sp}$.
  • Figure 3: The PQC $M_d$ used to make temperature predictions in the laser cutting use case. The first qubit represents $v$, and the subsequent qubits represent the $d$ neighboring nodes. The temperature of each node is embedded on to the corresponding qubit. This is followed by a parameterized layer, where the first qubit and the subsequent qubits are entangled. Finally, the expectation value of the first qubit is measured and then projected to a temperature value.