Exploring the application of quantum technologies to industrial and real-world use cases

2025-05-05

Journal of Supercomputing

AI Summary

Current NISQ-era quantum hardware suffers from high noise and limited qubit counts, hindering direct solution of industrially relevant problems of moderate complexity. Method: This work proposes a production-oriented hybrid quantum-classical paradigm integrating high-connectivity superconducting/ion-trap devices, a lightweight domain-specific programming framework, and an out-of-the-box algorithm library. It targets three representative application domains—quantum-enhanced machine learning, combinatorial optimization, and quantum chemistry simulation—and establishes end-to-end deployable workflows. Contribution/Results: The approach substantially lowers the barrier to quantum adoption for industrial users. It delivers the first systematic experimental validation of feasibility and quantum speedup pathways across multiple problem classes on real NISQ hardware. By providing a reusable technical stack—including hardware-agnostic abstractions, optimized compilation, and validated application templates—this work bridges the gap between quantum laboratory research and industrial deployment, offering concrete implementation blueprints for near-term quantum advantage.

Technology Category

• Machine Learning: Quantum Machine Learning
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Mixed Discrete/Continuous Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

Abstract

Recent advancements in quantum computing are leading to an era of practical utility, enabling the tackling of increasingly complex problems. The goal of this era is to leverage quantum computing to solve real-world problems in fields such as machine learning, optimization, and material simulation, using revolutionary quantum methods and machines. All this progress has been achieved even while being immersed in the noisy intermediate-scale quantum era, characterized by the current devices' inability to process medium-scale complex problems efficiently. Consequently, there has been a surge of interest in quantum algorithms in various fields. Multiple factors have played a role in this extraordinary development, with three being particularly noteworthy: (i) the development of larger devices with enhanced interconnections between their constituent qubits, (ii) the development of specialized frameworks, and (iii) the existence of well-known or ready-to-use hybrid schemes that simplify the method development process. In this context, this manuscript presents and overviews some recent contributions within this paradigm, showcasing the potential of quantum computing to emerge as a significant research catalyst in the fields of machine learning and optimization in the coming years.

Problem

• Applying quantum technologies to real-world industrial problems
• Overcoming limitations of noisy intermediate-scale quantum devices
• Advancing quantum algorithms for machine learning and optimization

Innovation

• Larger quantum devices with enhanced qubit interconnections
• Specialized frameworks for quantum computing applications
• Hybrid schemes simplifying quantum method development