REVIEW article
Front. Quantum Sci. Technol.
Sec. Quantum Computing and Simulation
Volume 4 - 2025 | doi: 10.3389/frqst.2025.1653104
Quantum Machine Learning early opportunities for the energy industry: a scoping review
Provisionally accepted
Francesco Strata*
Luca MiglioriNour GebranNicolina GuarinoGiacomo Carlo ColomboSara PezzuoloEmiliano Luzietti- PricewaterhouseCoopers Italia, Milan, Italy
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Quantum computing innovations have garnered significant attention for their potential to revolutionize industries, with the energy sector being one of the most promising areas for application. As global energy demand increases and sustainability becomes more critical, computational technologies offer groundbreaking solutions for energy production, storage, and distribution. In this landscape, quantum computing plays a crucial role in unlocking the full potential of artificial intelligence and machine learning, as the research and development in the quantum machine learning field grows constantly. In this paper, we present a scoping review of early quantum machine learning applications within the energy industry value chain. Starting from 34 sources, we analyze and discuss 22 use cases in the energy sector, thoroughly examining each one to understand its potential applications and impact. We then evaluate these early-stage quantum applications to determine their feasibility and benefits, offering insights into their relevance and effectiveness in the context of the industry's evolving landscape. This is done by introducing a novel framework: the Assessment Model for Innovation Management (AMIM). Our research highlights the opportunities quantum innovations present for the energy sector and offers actionable insights into which applications are the best investments and why. Overall, the feasibility and technological maturity of quantum machine learning use cases are still in the early stages, though their market compatibility and potential benefits are relatively high for most of them. This indicates that while quantum machine learning holds immense potential, further development is necessary to fully realize its benefits in the energy sector.
Keywords: Quantum computing, machine learning, artificial intelligence, Quantum machine learning, energy industry, sustainability, Innovation management, Industry applications
Received: 24 Jun 2025; Accepted: 08 Sep 2025.
Copyright: © 2025 Strata, Migliori, Gebran, Guarino, Colombo, Pezzuolo and Luzietti. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Francesco Strata, PricewaterhouseCoopers Italia, Milan, Italy
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.