Quantum Computing and Artificial Intelligence for Power System Modeling and Control

Samuel Yen-Chi Chen, Kuan‐Cheng Chen

Critical infrastructures (CI)-including power grids, financial systems, communication networks, and transportation networks-are essential to societal stability and economic security. As these systems grow increasingly complex and interconnected, they face mounting challenges in scalability, robustness, real-time decision-making, and data privacy. While classical artificial intelligence (AI) and machine learning (ML) techniques have been applied to enhance CI monitoring, optimization, and control, they are often constrained by data heterogeneity, centralized architectures, and limited adaptability. Quantum computing (QC), known for its potential to solve certain computational problems exponentially faster than classical methods, has opened new possibilities in this domain. Quantum artificial intelligence (QAI), which combines quantum computing with AI techniques, offers transformative opportunities, particularly through paradigms such as federated quantum machine learning (FedQML) and quantum reinforcement learning (QRL). These approaches promise privacy-preserving distributed intelligence and dynamic control across CI systems. In this article, we survey recent developments in QAI and explore their applications in critical infrastructure domains. We also highlight key challenges and outline a forward-looking roadmap toward resilient, secure, and quantum-enhanced infrastructure systems.

John G. Vlachogiannis

Dipti Chauhan, Pragya Ranka, Pritika Bahad +1 more

Quantum Artificial Intelligence is an exciting new area, which integrates quantum computing with artificial intelligence (AI) in order to produce increasingly sophisticated and effective computational methods, models, and systems. The goal is to use quantum mechanics properties like superposition and entanglement to boost AI's computational power and speed. Quantum computers use quantum bits (qubits) to encode information, in contrast to classical computers, which run on binary digits (bits). Quantum computers can perform some computations faster than classical computers because qubits can exist in multiple states simultaneously. In order to achieve superior computational power in the field of artificial intelligence, this article emphasizes the importance of utilizing quantum mechanical principles. It also explores the potential uses of quantum AI, including machine learning, data analysis, and optimization issues. It deals with the state of art of semiconductors in Quantum computing.

Jasgurpreet Singh Chohan, Jatinder Kumar, Krishna Kant Dixit +6 more

泽坤 曹

With the rapid development of artificial intelligence, computational systems are gradually evolving toward multi-modal integration and human-like reasoning. However, traditional computing architectures face fundamental limitations in computing power, error correction, and theoretical boundaries. Quantum computing, especially quantum entanglement and interference mechanisms, provides a potential path to overcome these limitations, but existing quantum error-correction theories remain incomplete and are difficult to integrate with artificial intelligence systems. This paper proposes a theoretical framework that combines an artificial intelligence virtual personality model with a quantum entanglement interference error-correction model. By introducing forward and backward wave-function entanglement, discrete-variable wave-function structures, and symmetric interference transmission, the model establishes a unified description of information propagation, correction, and stability. The framework further extends Boolean algebra, Turing computation models, and multi-modal intelligence growth into a cross-domain structure suitable for AI–quantum hybrid systems. In addition, this work explores the philosophical foundation of virtual personality, treating intelligence as the interaction between physical realization and virtual consciousness, and discusses the necessity of ethical and legal frameworks for artificial intelligence virtual personality. The proposed model aims to provide a theoretical basis for the transitional integration of artificial intelligence and quantum computing, and to support future development of quantum-enhanced intelligent systems.

Sævarsson, Brynjar