Quantum Artificial Intelligence (Quantum AI) suits quantum computing and artificial intelligence to tackle complex issues such as multi-dimensional optimization and massive volumes of information more proficiently than classic strategies. Quantum AI utilizes quantum phenomena like superposition, entanglement, and tunneling, among others, to realize a speedup of several orders of magnitude compared to classical AI algorithms. Quantum AI is capable of being implemented for healthcare, finance, and cybersecurity. Whereas quantum simulations are used for accelerating drug discovery and for improving the accuracy of molecular modeling, they also benefit precision medicine through advanced genomic analysis as well as diagnostics, including precision diagnostics, through the precision testing of natural disease biomarkers, active and inactive metabolites, and toxic blood chemistry. Quantum AI in finance enhances trading profitability by accelerating the prediction of real-time market trends, optimization of the portfolio, and financial fraud detection, while processing vast datasets. Quantum cryptography, e.g., quantum key distribution, provides security in cybersecurity, whereas quantum algorithms, such as Shor, bring threats to encryption, and thus post-quantum cryptography is developed. This chapter shows that Quantum AI pertains to practical feasibility, despite error correction, coherence times, and a lack of algorithm development. What we show is about how Quantum AI can transform these sectors, what the stumbling blocks are, hardware scalability, and algorithm efficiency, in addition to quantum computing reliability. This study, therefore, highlights the importance of continuing to invest in Quantum AI so that it can be fully realized.
Quantum Artificial Intelligence (Quantum AI) suits quantum computing and artificial intelligence to tackle complex issues such as multi-dimensional optimization and massive volumes of information more proficiently than classic strategies. Quantum AI utilizes quantum phenomena like superposition, entanglement, and tunneling, among others, to realize a speedup of several orders of magnitude compared to classical AI algorithms. Quantum AI is capable of being implemented for healthcare, finance, and cybersecurity. Whereas quantum simulations are used for accelerating drug discovery and for improving the accuracy of molecular modeling, they also benefit precision medicine through advanced genomic analysis as well as diagnostics, including precision diagnostics, through the precision testing of natural disease biomarkers, active and inactive metabolites, and toxic blood chemistry. Quantum AI in finance enhances trading profitability by accelerating the prediction of real-time market trends, optimization of the portfolio, and financial fraud detection, while processing vast datasets. Quantum cryptography, e.g., quantum key distribution, provides security in cybersecurity, whereas quantum algorithms, such as Shor, bring threats to encryption, and thus post-quantum cryptography is developed. This chapter shows that Quantum AI pertains to practical feasibility, despite error correction, coherence times, and a lack of algorithm development. What we show is about how Quantum AI can transform these sectors, what the stumbling blocks are, hardware scalability, and algorithm efficiency, in addition to quantum computing reliability. This study, therefore, highlights the importance of continuing to invest in Quantum AI so that it can be fully realized.
Quantum Artificial Intelligence (Quantum AI) is a popular and new computational field that incorporates the use of quantum computing in enhancing Artificial Intelligence (AI). This chapter addresses the use of Quantum AI in three sectors, namely healthcare, finance, and cybersecurity, with a view that the deployment of AI integrated with Quantum computing shall take the respective industry to another level. Quantum AI enhances diagnosis and medical imaging, pattern recognition, and diagnostics, which increases the rate of accuracy or detection and diagnosis of diseases and illnesses. Moreover, Quantum AI optimizes portfolio management, risk evaluation, and prediction, and fraud detection in the financial industry, as well as enhances the computational rate in comparison to current rates. Quantum is used to enhance investment solutions and approaches to assess large amounts of big data as soon as possible, resulting in more potent risk management tools. Also, they improve the detection of fraud in financial transactions by providing a means of detecting the variation in such transactions, making security in online/mobile banking and other facets of electronic payment firmer. On the same level, they defend computer networks and systems from cyber-attacks through massive and complicated structures of analyzing large data sets in an incredibly short period. Technology has certain drawbacks, which include issues in quantum computer hardware, errors that could be present in quantum structures, and moral issues relating to the safety of data and information. A solution to these challenges could only be good if Quantum AI becomes widely implemented across the numerous domains of industry today. This Chapter gives an elaborate overview of the relevance of Quantum AI in healthcare, finance, and cybersecurity, which defines the trend of intelligent and secure computing systems.
The convergence of quantum computing and artificial intelligence (AI) presents a paradigm shift in healthcare, revolutionizing complex biological simulations, genomic data processing, and drug discovery innovations. Traditional computational methods, despite their advancements, often struggle with the sheer scale and complexity of biological data, limiting the speed and accuracy of medical breakthroughs. Quantum computing, with its ability to process vast datasets exponentially faster than classical computers, coupled with AI's predictive capabilities, offers a transformative solution for accelerating biomedical research and clinical applications. This paper explores quantum machine learning’s role in optimizing AI-driven molecular dynamics simulations for drug discovery. By leveraging quantum-enhanced algorithms, researchers can rapidly model molecular interactions, analyze drug-receptor binding affinities, and predict pharmacokinetics with unprecedented precision. Additionally, we examine quantum-assisted deep learning models for deciphering intricate biological mechanisms such as protein folding, epigenetic modifications, and metabolic pathway interactions, enabling more accurate predictions of disease progression and therapeutic targets. Furthermore, the integration of AI-quantum hybrid models in clinical diagnostics and imaging analytics is redefining personalized medicine. Quantum-enhanced deep learning facilitates high-resolution medical imaging, real-time anomaly detection, and optimized radiomic feature extraction, leading to early and more accurate disease diagnosis. In genomics, quantum computing significantly accelerates whole-genome sequencing and mutation analysis, paving the way for tailored treatment strategies based on an individual's genetic profile. Despite its promise, challenges such as quantum hardware limitations, data coherence issues, and ethical considerations must be addressed to ensure the practical implementation of quantum-AI healthcare solutions. This paper provides a comprehensive analysis of the potential, challenges, and future directions of quantum-AI synergy in transforming modern healthcare.
Abstract Quantum computing and artificial intelligence (AI) are converging to reshape the future of healthcare. Modern medicine generates vast and complex datasets from imaging, genomics, wearable sensors, and real-time clinical records. While AI has already demonstrated significant potential in diagnostics, drug discovery, and personalised medicine, its progress is often constrained by high computational demand, limited interpretability, and energy inefficiency. Quantum computing offers a fundamentally different paradigm, using qubits, superposition, and entanglement to process high-dimensional data and explore solution spaces in parallel. This synergy-quantum-enhanced AI or quantum machine learning (QML)promises transformative capabilities across the healthcare continuum. This narrative review explores the foundations of quantum computing, the evolution of AI in medicine, and the points where these technologies intersect. Applications span a range of biomedical domains. In radiology and pathology, quantum-assisted classifiers can enhance both the speed and interpretability of image analysis. In drug discovery and genomics, variational quantum eigensolvers and hybrid quantum–classical models may accelerate molecular simulations and genome-wide association studies. In personalized medicine, quantum kernel methods enable more precise phenotype clustering and improve the accuracy of treatment response predictions. Operationally, quantum-AI integration also supports hospital logistics, ICU resource allocation, and supply-chain resilience. Despite these opportunities, significant challenges remain, including hardware limitations, data encoding bottlenecks, interpretability deficits, and regulatory ambiguity. Ethical concerns around privacy, equity, and compliance with frameworks such as HIPAA and GDPR must also be addressed. By mapping opportunities and risks, this review underscores that quantum-AI is no longer speculative but an emerging reality. Harnessing its potential requires multidisciplinary collaboration to translate qubits and algorithms into tangible improvements in patient outcomes, clinical trust, and system efficiency.
Quantum Computing (QC) and Artificial Intelligence (AI) have emerged as key technologies in the evolution of Industry 6.0, driving advancements in automation and advanced analytics, and process optimization. Their integration holds the potential to revolutionize sectors such as data science, healthcare, finance, and cybersecurity by enabling faster and more efficient computations through qubits, superposition, and quantum entanglement. However, the lack of structured knowledge regarding specific QC methodologies and applications in AI hinders its optimal implementation and development. Consequently, this study aims to identify the applications and variables associated with QC-AI integration. To this end, a systematic literature review was conducted following the PRISMA 2020 methodology, drawing on studies from Scopus and Web of Science databases. This enabled the analysis of trends, limitations, and opportunities in this technological convergence. This study aims to systematically examine the intersection of quantum computing and artificial intelligence by identifying the key technological features, integration requirements, and sectoral applications that define the current state of the field. The review contributes by mapping existing research, highlighting methodological approaches, and revealing gaps that may guide targeted advancements in hybrid quantum AI systems. The insights generated have the potential to accelerate innovation in high-impact domains such as healthcare, finance, energy, and cybersecurity. The findings indicate that the main advances in QC applied to AI focus on quantum optimization, Quantum Machine Learning (QML), and post-quantum cryptography. Notably, sectors such as energy, healthcare, and finance have shown significant progress in adopting these technologies. For example, in healthcare, QML has been applied to simulate molecular interactions to accelerate drug discovery, and in finance, it enhances predictive models for market behavior. The study concludes that although QC demonstrates substantial potential to enhance AI, its broader adoption remains constrained by reliance on NISQ hardware, the need for effective error correction, and the limited scalability of hybrid quantum classical algorithms. Addressing these challenges will be essential to establishing QML as a cornerstone of technological innovation and digital transformation. Additionally, this review introduces an integrative framework that categorizes key AI QC convergence dimensions and proposes a classification of application areas based on technical requirements and algorithmic capabilities. These contributions aim to guide future experimental validations and hybrid model development.
The convergence of quantum computing and artificial intelligence (AI) presents a paradigm shift in healthcare, revolutionizing complex biological simulations, genomic data processing, and drug discovery innovations. Traditional computational methods, despite their advancements, often struggle with the sheer scale and complexity of biological data, limiting the speed and accuracy of medical breakthroughs. Quantum computing, with its ability to process vast datasets exponentially faster than classical computers, coupled with AI\\\'s predictive capabilities, offers a transformative solution for accelerating biomedical research and clinical applications. This paper explores quantum machine learning's role in optimizing AI-driven molecular dynamics simulations for drug discovery. By leveraging quantum-enhanced algorithms, researchers can rapidly model molecular interactions, analyze drug- receptor binding affinities, and predict pharmacokinetics with unprecedented precision. Additionally, we examine quantum-assisted deep learning models for deciphering intricate biological mechanisms such as protein folding, epigenetic modifications, and metabolic pathway interactions, enabling more accurate predictions of disease progression and therapeutic targets. Furthermore, the integration of AI-quantum hybrid models in clinical diagnostics and imaging analytics is redefining personalized medicine. Quantum-enhanced deep learning facilitates high-resolution medical imaging, real-time anomaly detection, and optimized radiomic feature extraction, leading to early and more accurate disease diagnosis. In genomics, quantum computing significantly accelerates whole-genome sequencing and mutation analysis, paving the way for tailored treatment strategies based on an individual\\\'s genetic profile. Despite its promise, challenges such as quantum hardware limitations, data coherence issues, and ethical considerations must be addressed to ensure the practical implementation of quantum-AI healthcare solutions.
The integration of quantum computing and artificial intelligence represents a paradigm shift in the ability of medicine to solve complex challenges. Quantum computing's unparalleled processing power enables the simulation of molecular interactions, decoding of genomic data, and modeling of biological systems at a scale and speed unattainable by classical computers. When paired with advanced AI algorithms, this technological synergy accelerates drug discovery, enhances diagnostic precision, and enables the development of highly personalized therapies. Moreover, these technologies are poised to reduce the time and cost of drug development, improve patient outcomes, and facilitate real-time clinical decision-making. This chapter highlights the importance of promoting the joint application of quantum computing and artificial intelligence to solve complex medical problems. Despite the progress achieved to date, there are current challenges to achieving widespread applications and prospects for this research topic.