In the twenty-first century, humanity is compelled to face global challenges. Such challenges involve complex systems. However, science has some cognitive and predictive limits in dealing with complex systems. Some of these limits are related to computational complexity and the recognition of variable patterns. To overcome these limits, artificial intelligence (AI) and quantum computing (QC) appear to be helpful. Even more promising is quantum AI (QAI), which emerged from the combination of AI and QC. The combination of AI and QC produces reciprocal, synergistic effects. This work describes some of these effects. It shows that QC offers new materials for implementing AI and innovative algorithms for solving optimisation problems and enhancing machine learning algorithms. Additionally, it demonstrates how AI algorithms can help overcome many of the experimental challenges associated with implementing QC. It also outlines several perspectives for the future development of quantum artificial intelligence.
In the twenty-first century, humanity is compelled to face global challenges. Such challenges involve complex systems. However, science has some cognitive and predictive limits in dealing with complex systems. Some of these limits are related to computational complexity and the recognition of variable patterns. To overcome these limits, artificial intelligence (AI) and quantum computing (QC) appear to be helpful. Even more promising is quantum AI (QAI), which emerged from the combination of AI and QC. The combination of AI and QC produces reciprocal, synergistic effects. This work describes some of these effects. It shows that QC offers new materials for implementing AI and innovative algorithms for solving optimisation problems and enhancing machine learning algorithms. Additionally, it demonstrates how AI algorithms can help overcome many of the experimental challenges associated with implementing QC. It also outlines several perspectives for the future development of quantum artificial intelligence.
The convergence of quantum computing and artificial intelligence has introduced a new paradigm in computational science known as Quantum Artificial Intelligence (QAI). By leveraging quantum mechanical principles such as superposition, entanglement, and quantum parallelism, QAI aims to overcome the limitations of classical machine learning, particularly in handling high-dimensional data, complex optimization, and scalability issues. This paper presents a comprehensive review of foundational concepts in both classical machine learning and quantum computing, followed by an in-depth discussion of emerging quantum algorithms tailored for AI applications, such as quantum neural networks, quantum support vector machines, and variational quantum classifiers. We explore the practical implications of these approaches across key sectors, including healthcare, finance, cybersecurity, and logistics. Furthermore, we identify critical challenges related to hardware limitations, algorithmic stability, data encoding, and ethical considerations. Finally, we outline research directions necessary to advance the field, highlighting the transformative potential of QAI in shaping the next generation of intelligent technologies
Artificial Intelligence (AI) continues to drive innovation across industries, but faces mounting challenges in computational scalability, optimization, and interpretability as models grow increasingly complex. Quantum Artificial Intelligence (QAI) offers a promising path forward by leveraging quantum phenomena such as superposition and entanglement to augment learning processes beyond classical limits. This review synthesizes the recent progress in QAI, emphasizing a comparative perspective framed through its advantages and limitations. It examines how hybrid quantum-classical architectures and variational models seek to address constraints in the Noisy Intermediate-Scale Quantum (NISQ) era, and how applications in healthcare, finance, and cybersecurity illustrate both the potential and the barriers of the field. While several studies have broadly mapped the growth of QAI in algorithms and applications, this paper adopts a different perspective: emphasizing comparative analysis through advantages and limitations. The focus is on where QAI offers unique opportunities, where it encounters obstacles distinct from classical AI and quantum computing, and what research directions may transform these challenges into breakthroughs. By situating QAI within this comparative framework, the paper provides a realistic yet forward-looking assessment of its trajectory as a paradigm poised to reshape computational intelligence.
Quantum Artificial Intelligence (QAI) combines quantum computing with artificial intelligence (AI) to surpass the traditional barriers of classical computing systems, such as their inability to solve large-scale optimization problems or simulate complex quantum systems, which require exponentially increasing computational resources. Quantum computing, on the other hand, makes use of quantum mechanical phenomena of superposition, entanglement, and interference to process millions of data points in parallel, avoiding the exponential computation needed in many complex problems. QAI combines principles of quantum computing with AI techniques, thereby accelerating model learning. Some of the most notable applications include drug discovery in terms of exact predictions of molecular interactions, due to the enhancement of quantum simulations. There are cryptographic applications where QAI challenges classical encryption but also contains more advanced, non-breakable quantum cryptographic methods. In the same way as general advances in the field of data analysis and optimization, these domains would change: financial modeling, cybersecurity, and climate science. The biggest challenges remaining in scaling quantum hardware are noise and decoherence. Solutions will require major advances in quantum error correction, along with hybrid quantum-classical systems integrating the strengths of each. At this point, added to the list of critical concerns in the development of QAI are ethical considerations such as data privacy, algorithmic bias, and transparency. But all this notwithstanding, QAI has the transformative potential to solve problems that classical AI was unable to solve and drive innovation in domains ranging from healthcare and environmental science to cryptography and beyond. Unlocking its full capabilities and overcoming the resultant technical and ethical challenges is by way of further research and collaboration.
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.
There is a major promise of extensive technical disturbance made by increasing convergence of two rapid development areas: Quantum Computing & Artificial Intelligence (AI). Unlike traditional computers, quantum computer use quantum -mechanical concepts as complication & superposition towards process data in a new way. Because of this, quantum computer machines can improve traditional systems towards solve certain types of problems. Machine learning (ML) algorithm is a sub -region of artificial intelligence aimed at automatically analyzing, interpreting & predicting procedures on a dataset. It is expected that the merger of AI & quantum calculation can quickly increase the processor power available for machine learning, which will accelerate processes such as data analysis, mimic quantum systems & handle complex customization problems. Medication development, material research & encryption abide some of the areas that benefit from the correction of quantum calculation of AI. among Quantum-Enhanced AI, the training machine learning models can endure very quick & more effective, so we can take problems that were impossible in the past. Pattern recognition, classification & decision -making artificial intelligence abide corners of applications; Quanta algorithms can also provide better ways for these features. Nevertheless, there abide still obstacles towards removing, such as creating special quantum algorithms for AI functions, ensuring that quantum systems can fix incorrectly, & make quantum machine scalable. Despite these challenges, the combination of AI & quantum calculation has the opportunity towards enter a new era of technical progress, which has not been able towards solve traditional data processing by answering major, complex problems so far.
Abstract Artificial intelligence (AI) and quantum computing represent a new frontier in computing, offering the promise of solving problems previously considered unsolvable. This article aims to provide an in-depth analysis of the current state of research at the intersection of quantum computing and artificial intelligence. We will explore the basic concepts, major applications, and challenges of quantum machine learning (QML). We will start by offering a definition of the motivation for the convergence of quantum computing and artificial intelligence. We will then proceed to offer a description of the basic algorithmic frameworks for quantum machine learning, such as quantum differential algorithms (VQA) for near-term quantum (NISQ) devices and the prospects for error-tolerant quantum algorithms such as the Harrow-Hassidim-Lloyd algorithm. We will then proceed to offer a methodical analysis of the current applications of quantum machine learning in finance, cybersecurity, materials science, among other fields. We will then proceed to discuss the basic challenges facing the implementation of quantum machine learning, while offering a glimpse of the future of quantum-enhanced artificial intelligence.