Review Article: Quantum-AI Convergence for Smart Grid Cybersecurity: A Systematic Review of Emerging Technologies, Threats, and Resilient Architectures
Abstract
Smart grids have revolutionized the way electricity is generated, transmitted, and consumed by utilizing various technologies, including artificial intelligence, IoT devices, advanced communication networks, and distributed energy resources, which have enhanced the efficiency, reliability, and sustainability of modern power systems. This interconnected ecosystem, however, has multiplied the potential attack surface, and quantum computing poses a new risk to the traditional means of securing encoded data, requiring quantum-resilient security solutions. Many reviews focus on a specific aspect of AI, PQC, quantum computing, or smart grid cybersecurity. Following the 2024 PRISMA extension to the PRISMA 2020 methodology, this review systematically analyzed peer reviewed studies published between 2024 to 2026, retrieved from IEEE Xplore, Scopus, Web of Science, ScienceDirect, SpringerLink, ACM Digital Library, Wiley Online Library, MDPI, and Google Scholar. A thematic analysis of 24 selected studies showed that the major research areas were Artificial Intelligence and Machine Learning (58.3%), Quantum Computing and Quantum Security (50.0%) and Smart Grid Cybersecurity (41.7%). The literature reviewed was evenly distributed between PQC (25.0%) and IoT/CPS security (25.0%), while the other topics, namely Quantum Key Distribution, Digital Twins, Blockchain, and Quantum Reinforcement Learning, were comparatively less explored with 8.3% in each study. The review brings together…
How strong is the evidence?
Evidence Hub reads this as a systematic review. Its evidence level is 1 of 5 (Highest · pooled evidence). Pooled evidence from several studies sits at the top of the evidence pyramid.
Read the paper
Related studies
- Chaos and quantum thermalization
- The Dalton quantum chemistry program system
- Cord Blood Leptin and Adiponectin as Predictors of Adiposity in Children at 3 Years of Age: A Prospective Cohort Study
- Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes
- Stable adiabatic quantum batteries
- A novel self-lipid antigen targets human T cells against CD1c(+) leukemias
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.