Quantum artificial intelligence is an important step forward in the relationship with both quantum computing and AI. This paper investigates the use of quantum computing technique as an augmentation to AI capabilities efficiencies in data retrieval, model development, and problem resolution. A very general description is provided of self-consistence concepts of quantum computing, the main milestones in the field of quantum artificial intelligence, and practical analogs in a range of areas. Algorithm development, the development of hardware, and ethics are the obstacles that are mentioned, which then give some perspectives concerning the future within this new field.
Quantum artificial intelligence is an important step forward in the relationship with both quantum computing and AI. This paper investigates the use of quantum computing technique as an augmentation to AI capabilities efficiencies in data retrieval, model development, and problem resolution. A very general description is provided of self-consistence concepts of quantum computing, the main milestones in the field of quantum artificial intelligence, and practical analogs in a range of areas. Algorithm development, the development of hardware, and ethics are the obstacles that are mentioned, which then give some perspectives concerning the future within this new field.
Quantum computing and artificial intelligence (AI) are two rapidly evolving technologies poised to redefine the future of computation and decision-making.The two fields unite under the name Quantum Artificial Intelligence (QAI) or Quantum Intelligence, which enables a transformative approach to problem-solving through the fusion of AI mental functions with quantum computing capabilities.This paper provides a comprehensive review of the current landscape, highlighting the opportunities and challenges in integrating quantum computing with AI systems.The research investigates current developments in quantum machine learning, quantum neural networks, and hybrid quantum-classical systems, which show promise for achieving rapid performance improvements and enhanced scalability and data processing capabilities.At the same time, it identifies critical barriers, including hardware limitations, algorithmic complexity, data encoding issues, and the lack of standardization in frameworks.The review further explores future directions, including the role of explainable AI, error correction, and interdisciplinary research in achieving practical quantum-AI synergy.By analyzing both the technological potential and the practical constraints, this paper aims to outline a roadmap for advancing quantum intelligence as a transformative force in next-generation computational paradigms.
Artificial Intelligence (AI) has been evolving at a very rapid pace, with increase in compute power, thus reshaping how complex problems are handled and interpreted from vast amounts of information. AI and newly emerging Quantum Computing have each made significant strides in recent years. Quantum computing has surfaced as a promising new paradigm that leverages on unique quantum phenomena such as like superposition and entanglement to potentially deliver dramatic gains in computational power. Increasingly, researchers are combining these distinct fields into "quantum AI," exploring ways to harness quantum devices for data-intensive tasks, advanced optimisation, and even tentative investigations into emergent machine "awareness." Already researchers have established the quantum advantage over classical AI. This paper outlines the fundamental aspects of AI with respect to quantum computing. It also surveys possible benefits for deep learning and optimization, and considers key concerns related to hardware readiness, algorithmic design, and ethical frameworks. By mapping current progress and pinpointing ongoing hurdles, we provide an entry-level guide to quantum AI's prospects and the steps required for its real-world deployment.
Artificial intelligence (AI) advancements over the past few years have had an unprecedented and revolutionary impact across everyday application areas. Its significance also extends to technical challenges within science and engineering, including the nascent field of quantum computing (QC). The counterintuitive nature and high-dimensional mathematics of QC make it a prime candidate for AI's data-driven learning capabilities, and in fact, many of QC's biggest scaling challenges may ultimately rest on developments in AI. However, bringing leading techniques from AI to QC requires drawing on disparate expertise from arguably two of the most advanced and esoteric areas of computer science. Here we aim to encourage this cross-pollination by reviewing how state-of-the-art AI techniques are already advancing challenges across the hardware and software stack needed to develop useful QC - from device design to applications. We then close by examining its future opportunities and obstacles in this space.
Quantum computing and artificial intelligence (AI) are two of the most transformative technologies of the 21st century. The convergence of these fields promises unprecedented advancements in computational capabilities, enabling solutions to complex problems that are currently intractable for classical computers. This paper explores the intersection of quantum computing and AI, examining the potential impacts, current research, challenges, and future directions. By leveraging quantum phenomena such as superposition, entanglement, and quantum parallelism, quantum computing can enhance AI algorithms, offering exponential speedups and new methodologies for machine learning, optimization, and data analysis.
The fast development of(AI) has radically changed the contemporary industries with its smart automation, predictive analytics, and decision-making based on data. However, the increasing complexity and scale of AI systems raise severe concerns of computational limits, the effectiveness of optimization, and huge amounts of data processing. The most important concepts that quantum computing as an emergent, disruptive paradigm is supposed to solve are superposition, entanglement and quantum parallelism. This chapter is a detailed talk on the convergence of quantum computing and AI which is commonly referred to as Quantum Artificial Intelligence and how it can enhance the effectiveness of machine learning, reduce the computation time, and address difficult optimization problems that are incapable of being resolved using classical systems. It also has an overview of how quantum-enhanced AI is used in addressing different fields, including healthcare, finance, cybersecurity, and logistics, and addresses such important concerns as hardware constraints, noise sensitivity, and scalability.
Quantum computing is emerging as a powerful computational paradigm capable of solving infeasible problems for classical computers. This review explores the intersection of quantum computing and artificial intelligence (AI), focusing on how quantum algorithms can enhance AI applications. The purpose of this review is to provide a comprehensive analysis of current advancements in quantum machine learning (QML), quantum optimization, and quantum neural networks, highlighting their implications for data processing, cryptography, and decision-making systems. The scope of the literature includes peer-reviewed articles, conference papers, and technical reports from 2020 to 2025, focusing on quantum algorithms, applications in AI, and hardware limitations. Key findings indicate that quantum computing can significantly accelerate data processing tasks, optimize complex problem-solving techniques, and improve the efficiency of AI models. However, challenges such as quantum noise, error correction, and hardware scalability remain substantial barriers to widespread adoption. This review concludes with discussing future research directions, including hybrid quantum-classical systems and advanced quantum error correction methods. This study serves as a foundational reference for researchers and practitioners aiming to leverage quantum computing for AI-driven applications. 3. Keywords Quantum Computing, Artificial Intelligence, Quantum Machine Learning, Quantum Algorithms, Data Processing, Optimization, Quantum Neural Networks