In this paper we describe a complete quantum computational model of computation that is based on the production system. The presented description of the model does not require any quantum computation background. By doing so, the reason that quantum c
Quantum computing and artificial intelligence are two highly topical fields of research that can benefit from each other’s discoveries by opening a completely new scenario in computation, that of quantum artificial intelligence. Indeed, on the one hand, artificial intelligence algorithms can be made computationally more efficient due to the potential speedup enabled by quantum phenomena; on the other hand, the complex development of quantum computing technologies and methodologies can be properly supported by the use of classical artificial intelligence approaches. The “entanglement” of these two disciplines is opening up completely new directions in computer science research, and this survey aims to provide a systematic and taxonomic overview of the work that has already been done and that which will begin in the near future.
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.
The extraordinary advance in quantum computation leads us to believe that, in the not-too-distant future, quantum systems will surpass classical systems. Moreover, the field’s rapid growth has resulted in the development of many critical tools, including programmable machines (quantum computers) that execute quantum algorithms and the burgeoning field of quantum machine learning, which investigates the possibility of faster computation than traditional machine learning. In this paper, we provide a thorough examination of quantum computing from the perspective of a physicist. The purpose is to give laypeople and scientists a broad but in-depth understanding of the area. We also recommend charts that summarize the field’s diversions to put the whole field into context.
Mission critical (MC) applications such as defense operations, energy management, cybersecurity, and aerospace control require reliable, deterministic, and low-latency decision making under uncertainty. Although the classical Artificial Intelligence (AI) approaches are effective, they often struggle to meet the stringent constraints of robustness, timing, explainability, and safety in the MC domains. Quantum Artificial Intelligence (QAI), the fusion of artificial intelligence and quantum computing (QC), can potentially provide transformative solutions to the challenges faced by classical ML models. QAI is a broader umbrella than Quantum Machine Learning (QML) and additionally includes quantum optimization, search, and reasoning; we use QAI throughout the paper for the field at large, and QML only for learning-specific subroutines. The principal contributions of this work are: (i) a systematic survey of QAI methods analyzed through the lens of MC requirements like certification, robustness, and timing; (ii) a conceptual quantum cloud resource management and scheduling framework with deployment assumptions, complexity analysis, and failure-mode discussion; and (iii) an identification of the gaps between current QAI capabilities and MC systems requirements. We also propose a conceptual model for management of quantum resources and scheduling of applications driven by timeliness constraints. We discuss multiple challenges, including trainability limits, data access, and loading bottlenecks, verification of quantum components, and adversarial QAI. Finally, we outline future research directions toward achieving interpretable, scalable, and hardware-feasible QAI models for MC application deployment.
Artificial Intelligence (AI) has developed into a disruptive technology that now impacts multiple industries including healthcare and finance and education and scientific research. Recent developments in Generative Artificial Intelligence (GenAI) and Explainable Artificial Intelligence (XAI) and Agentic AI have enabled intelligent systems to operate beyond their typical predictive analytics functions. GenAI enables machines to produce original content which includes text and images and code through its extensive models and deep learning systems while XAI develops methods to make complex AI systems more understandable to users. Agentic AI improves the field by creating systems which can operate as independent units that make decisions based on their objectives and ongoing environmental changes. The current progress still faces major obstacles because the system needs to solve problems related to computing power and understanding models and expanding systems and making autonomous AI decisions dependable. This chapter investigates quantum computing which functions as a new computing method that can solve these specific problems. Quantum computing applies quantum parallelism and quantum optimization methods to achieve faster processing speeds for complex data and model training tasks which modern AI systems require. The combination of quantum computing with GenAI and XAI and Agentic AI will enhance model performance while creating better interpretability systems and developing stronger autonomous reasoning capabilities. The chapter shows how quantum-enhanced algorithms enable researchers to conduct large-scale data analysis while showing results more clearly and improving decision-making abilities of autonomous AI systems. The research demonstrates how advanced AI systems are combining with quantum technologies and establishes a new conceptual framework for future intelligent systems. The future of AI will transform through the combination of GenAI with XAI and Agentic AI and quantum computing which will create intelligent systems that operate with high autonomy while maintaining complete system transparency to solve complex real-world challenges.