Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Quantum Computing -- Strategic Recommendations for the Industry

Marvin Erdmann, Lukas Karch, Abhishek Awasthi, Caitlin Isobel Jones, Pallavi Bhardwaj, Florian Krellner, Jonas Stein, Claudia Linnhoff-Popien, Nico Kraus, Peter Eder, Sarah Braun, Tong Liu

TL;DR

The paper investigates industrial applicability of quantum computing for optimization and machine learning, focusing on hybrid quantum–classical solutions and benchmarking across vendors using a traffic-light framework. It compiles public hardware roadmaps for superconducting and ion-trap technologies, highlighting qubit counts as a core metric while acknowledging broader hardware and software maturity factors. Through a suite of industrial use cases in production scheduling, logistics, and ML, it delivers category-specific verdicts that reveal where quantum approaches are promising, where hybrid methods are advantageous, and where classical methods remain superior. The study emphasizes solver-aware problem modeling, decomposition techniques, and close collaboration with hardware providers to maximize near-term impact, with timelines suggesting meaningful progress around 2030–2035 as fault-tolerant hardware becomes more prevalent. Overall, the work offers a practical blueprint for industry to adopt quantum tools, guided by benchmarking results, roadmap expectations, and a clear emphasis on remaining challenges and transition pathways.

Abstract

This whitepaper surveys the current landscape and short- to mid-term prospects for quantum-enabled optimization and machine learning use cases in industrial settings. Grounded in the QCHALLenge program, it synthesizes hardware trajectories from different quantum architectures and providers, and assesses their maturity and potential for real-world use cases under a standardized traffic-light evaluation framework. We provide a concise summary of relevant hardware roadmaps, distinguishing superconducting and ion-trap technologies, their current states, modalities, and projected scaling trajectories. The core of the presented work are the use case evaluations in the domains of optimization problems and machine learning applications. For the conducted experiments, we apply a consistent set of evaluation criteria (model formulation, scalability, solution quality, runtime, and transferability) which are assessed in a shared system of three categories, ranging from optimistic (solutions produced by quantum computers are competitive with classical methods and/or a clear path to a quantum advantage is shown) to pessimistic (significant hurdles prevent practical application of quantum solutions now and potentially in the future). The resulting verdicts illuminate where quantum approaches currently offer promise, where hybrid classical-quantum strategies are most viable, and where classical methods are expected to remain superior.

Quantum Computing -- Strategic Recommendations for the Industry

TL;DR

The paper investigates industrial applicability of quantum computing for optimization and machine learning, focusing on hybrid quantum–classical solutions and benchmarking across vendors using a traffic-light framework. It compiles public hardware roadmaps for superconducting and ion-trap technologies, highlighting qubit counts as a core metric while acknowledging broader hardware and software maturity factors. Through a suite of industrial use cases in production scheduling, logistics, and ML, it delivers category-specific verdicts that reveal where quantum approaches are promising, where hybrid methods are advantageous, and where classical methods remain superior. The study emphasizes solver-aware problem modeling, decomposition techniques, and close collaboration with hardware providers to maximize near-term impact, with timelines suggesting meaningful progress around 2030–2035 as fault-tolerant hardware becomes more prevalent. Overall, the work offers a practical blueprint for industry to adopt quantum tools, guided by benchmarking results, roadmap expectations, and a clear emphasis on remaining challenges and transition pathways.

Abstract

This whitepaper surveys the current landscape and short- to mid-term prospects for quantum-enabled optimization and machine learning use cases in industrial settings. Grounded in the QCHALLenge program, it synthesizes hardware trajectories from different quantum architectures and providers, and assesses their maturity and potential for real-world use cases under a standardized traffic-light evaluation framework. We provide a concise summary of relevant hardware roadmaps, distinguishing superconducting and ion-trap technologies, their current states, modalities, and projected scaling trajectories. The core of the presented work are the use case evaluations in the domains of optimization problems and machine learning applications. For the conducted experiments, we apply a consistent set of evaluation criteria (model formulation, scalability, solution quality, runtime, and transferability) which are assessed in a shared system of three categories, ranging from optimistic (solutions produced by quantum computers are competitive with classical methods and/or a clear path to a quantum advantage is shown) to pessimistic (significant hurdles prevent practical application of quantum solutions now and potentially in the future). The resulting verdicts illuminate where quantum approaches currently offer promise, where hybrid classical-quantum strategies are most viable, and where classical methods are expected to remain superior.
Paper Structure (47 sections, 14 figures)

This paper contains 47 sections, 14 figures.

Table of Contents

  1. Introduction
  2. Quantum Hardware Roadmap
  3. Superconducting Quantum Computers
  4. Quantum Annealers
  5. Gate-based Devices
  6. Ion Traps
  7. Industrial Use Cases Considered
  8. Traffic-Light-based Evaluation
  9. Considered Aspects of Use Cases
  10. Categories, Evaluation, and Verdict
  11. Optimization Use Cases
  12. Production Assignment and Scheduling
  13. Traffic-Light-based Evaluation
  14. Verdict
  15. Train Routing
  16. ...and 32 more sections

Figures (14)

  • Figure 1: Historical and projected qubit count development of D-Wave's quantum annealing devices (blue) compared with qubit counts of devices manufactured by NEC (orange) and Qilimanjaro (green).
  • Figure 2: Historic and projected development of the physical qubit counts of gate-based quantum computers manufactured by IBM (blue), Google (orange), and IQM (green)
  • Figure 3: Historic and projected development of trapped-ion-based quantum computers manufactured by Quantinuum (blue) and IonQ (orange), compared with qubit counts of devices manufactured by AQT (green).
  • Figure 4: An exemplary solution to a problem with 16 jobs and 4 machines. The idle times of the machines between adjacent jobs depict the required set-up times. The objective is to reduce the total setup time, uniformly distribute the busy times of all machines, and maximize the total value PAS.
  • Figure 5: Microscopic illustration of a part of the Norwegian railway network. The colored circles represent the positions of the trains. Visualization based on data from railOscope.
  • ...and 9 more figures