Integrating artificial intelligence and quantum computing: A systematic literature review of features and applications

As wireless networks are getting increasingly complex, there is a strong need to develop novel approaches and capabilities for high throughput communication and processing of wireless data. This need is more pressing as unmanned aircraft vehicles (UAVs) are applied in new areas and innovations such as UAV (Unmanned Aerial Vehicle) swarms are developed. Millimeter-wave helps to meet the throughput demand as it provides the higher bandwidth that is needed for 5G wireless communication and beyond. But these higher frequencies also come with their own challenges, such as low signal penetration depth. The main challenge in modern wireless networks is to be able to accurately predict the dynamically changing radio environment. With the introduction of narrow beams in the mm-wave frequency, tracking of the beams is a great challenge. Artificial Intelligence (AI) and Quantum computing (QC) could be employed to resolve this problem of beam tracking. In this talk, the challenges in mm-wave wireless networks for vehicle-to-vehicle communication will be highlighted and the ideas involving recent developments in phased-array antenna, beam-steering, beam-alignment, and tracking will be discussed. We shall then critically evaluate the need for Artificial Intelligence (AI) and Quantum computing (QC) within the network architecture to provide the required capability for tasks such as beam-control, data-feed processing, resource, and interference management. The next-generation wireless networks need to be self-predictive and proactive to handle futuristic applications like holographic communication, haptic feedback, or any latency-dependent application. Especially in the context of rapidly changing environment where the UAVs are moving fast, AI and/or QC would play a crucial role to achieve a dynamically adaptive network. Next-generation wireless networks offer more capacity for the conveyance of relevant information from onboard instruments such as cameras or thermal sensors. Thus, there shall be a need for the efficient processing of potentially copious amounts of raw data for relevant information. For example, to deploy a drone swarm in a search and rescue operation, the use of QC or AI could improve the processing of received visual information with respect to processing speed. The application of AI and QC for mm Wave-UAV communications is a promising research direction to break through the traditional communication paradigm and integrate communication, computing, and storage resources.

Abstract— Studies in the literature argue that solving the memory problem of quantum computers (QC) will lead to groundbreaking developments in artificial intelligence algorithms affecting every process, strategy, business, and polic,es. Quantum adaptive algorithms have been widely applied, which improve existing methods by taking advantage of artificial intelligence and quantum computing. Although new developments are achieved with hybrid quantum systems, which aim to have sub-threads made by quantum computers, it cannot be predicted precisely where these studies will reach in the future and what kind of benefits and risks, they involve. In this study, based on the knowledge of contemporary and interdisciplinary literature, the current and potential uses of quantum computers, super artificial intelligence applications, and quantum computing methods on machine learning, whose applications have just begun in the laboratory environment, are examined, and assessed. It is claimed that the hardware disadvantages of quantum computers will disappear, and new quantum applications will be developed in the future. This is a requirement of adaptation to the prevalent usage of IoT in Industry 4.0 and Society 5.0 applications. Therefore, theoretically, and conceptual aspects of AI and quantum computing are evaluated to realize guidance of the miracles of the Prophets that can be discovered in the future.

This review explores the intricate and evolving relationship between Artificial Intelligence (AI) and Quantum Computing within the realm of financial markets. As technology continues to advance, the integration of AI and quantum computing has emerged as a paradigm-shifting force, promising unprecedented capabilities to analyze and navigate the complexities of financial systems. This critical review delves into the synergies, challenges, and potential disruptions arising from the intersection of these two transformative technologies. The utilization of AI in financial markets has witnessed remarkable progress in recent years, with machine learning algorithms, deep neural networks, and natural language processing contributing to enhanced data analysis, predictive modeling, and decision-making. However, the computational demands of these sophisticated algorithms often surpass the capabilities of classical computing architectures, paving the way for the exploration of quantum computing as a potential solution. Quantum computing, with its ability to process vast datasets and perform complex calculations at speeds inconceivable by classical computers, presents a revolutionary approach to addressing the computational challenges faced by AI in financial applications. The review critically examines the potential advantages of quantum computing, such as its capacity to solve optimization problems, simulate financial scenarios, and secure data through quantum cryptography. Despite the promises, the integration of AI and quantum computing in financial markets is not without hurdles. The review investigates the current limitations, including hardware constraints, error correction challenges, and the high costs associated with quantum computing infrastructure. Ethical considerations and regulatory frameworks surrounding the implementation of such powerful technologies in financial decision-making also warrant careful examination. This critical review provides a comprehensive analysis of the intersection of AI and quantum computing in financial markets, shedding light on the transformative potential, challenges, and ethical implications that accompany this cutting-edge convergence of technologies. Understanding this intersection is crucial for stakeholders seeking to navigate the evolving landscape of finance and technology.
 Keywords: AI, Quantum, Computing, Financial Market, Review.

Artificial intelligence (AI) and quantum computing will change the course of new drug discovery and approval. By generating computational data, predicting the efficacy of pharmaceuticals, and assessing their safety, AI and quantum computing can accelerate and optimize the process of identifying potential drug candidates. In this viewpoint, we demonstrate how computational models obtained from digital computers, AI, and quantum computing can reduce the number of laboratory and animal experiments; thus, computer-aided drug development can help to provide safe and effective combinations while minimizing the costs and time in drug development. To support this argument, 83 academic publications were reviewed, pharmaceutical manufacturers were interviewed, and AI was used to run computational data for determining the toxicity of collagen as a case example. The research evidence to date has mainly focused on the ability to create computational in silico data for comparison to actual laboratory data and the use of these data to discover or approve newly discovered drugs. In this context, “in silico” describes scientific studies performed using computer algorithms, simulations, or digital models to analyze biological, chemical, or physical processes without the need for laboratory (in vitro) or live (in vivo) experiments. Digital computers, AI, and quantum computing offer unique capabilities to tackle complex problems in drug discovery, which is a critical challenge in pharmaceutical research. Regulatory agents will need to adapt to these new technologies. Regulatory processes may become more streamlined, using adaptive clinical trials, accelerating pathways, and better integrating digital data to reduce the time and cost of bringing new drugs to market. Computational data methods could be used to reduce the cost and time involved in experimental drug discovery, allowing researchers to simulate biological interactions and screen large compound libraries more efficiently. Creating in silico data for drug discovery involves several stages, each using specific methods such as simulations, synthetic data generation, data augmentation, and tools to generate, collect, and affect human interaction to identify and develop new drugs.

At the nexus of artificial intelligence (AI) and quantum computing lies the emerging field of quantum machine learning (QML). By speeding up the computation of intricate algorithms, quantum computers have the potential to transform a number of fields, including machine learning, by outperforming classical computers by an exponential amount in specific tasks. This essay examines the fundamental ideas of quantum computing, how it applies to machine learning, and the potential advantages and difficulties of QML. We examine several quantum algorithms, including quantum versions of support vector machines, clustering, and neural networks, that can improve machine learning models. We also go over QML's drawbacks, present research directions, and potential future developments, providing insights into how quantum technologies might transform AI in the ensuing decades.With the potential to outperform traditional supercomputers in resolving important issues in a variety of fields, including machine learning, quantum computing has become a ground-breaking technology. This study investigates the fascinating nexus between artificial intelligence and quantum computing, looking at how quantum machine learning might revolutionize classification, pattern recognition, and data processing.

In this article, we will try to present from an academic point of view some of the relevant characteristics of both artificial intelligence (AI) and quantum computing (QC) in order to explore the possibility of a meaningful cooperation between both areas of computer science (CS). The quantum part of this paper is based on “The Quantum Circuit Model” which, in the opinion of the author, could be easier understood by computer scientists and/or artificial intelligence researchers, rather than other approaches such as, for example, “Adiabatic Quantum Computation”. Many fundamental questions will arise along this paper for which we will need to give an answer in order to analyze the basic principles that allow researchers and engineers to put AI and QC working together. With this in mind, we will briefly describe the behavior of the biological brain focusing on the identification of the singularities that allow them to perform in such an efficient manner. Energy consumption and parallelism are in the core of the above-mentioned efficiency. Then we will present some of the well-established artificial intelligence approaches that are potentially related to the operation of biological brains. After identifying common characteristics we will introduce basic concepts and issues related to the so-called reversible computing and QC that may eventually help to increase the efficiency of our current intelligent systems. In this respect, we will pay special attention to ‘speed’ and ‘energy consumption’. Some examples, as well as an outline of algorithms from both quantum computing and artificial intelligence, will be used to illustrate the ideas presented in the paper. We conclude with a discussion about what can we expect from the cooperation between both, apparently unconnected fields of computer science, artificial intelligence and quantum computing.

The optimization of independent automotive suspension systems, which is one of the main pillars of the vehicle performance and comfort, is currently going through a revolutionary change due to the development of artificial intelligence and quantum computing. This paper aims to review the multi-objective optimization of suspension parameters including camber, caster, and toe to discuss the complex design issues that arise from geometric and dynamic considerations. Some of the most common computational methodologies, which are Genetic Algorithms, Particle Swarm Optimization, and Gradient Descent, are discussed in this paper along with the new quantum computing techniques such as Gate-Based quantum computing and Quantum Annealing (QA). In addition, this review incorporates information from the practice of automotive manufacturers who have incorporated the use of artificial intelligence and quantum computing in their suspension systems. However, there are still some issues remaining, such as the computational cost, real-time flexibility, and the applicability of theoretical concepts to actual engineering structures. Some potential future research directions are introduced in this paper, such as hybrid optimization approaches, quantum techniques, and adaptive materials, which are considered as potential directions for future development. This systematic review presents a conceptual framework for researchers and engineers to follow, stressing the importance of interdisciplinarity in the development of intelligent suspension systems with performance objectives that are capable of adjusting to various road conditions. The findings of this work underscore the growing importance of complex computational techniques in modern automotive industry and highlight their potential to shape future developments based on emerging trends and industry practices.

Simulating many-body open quantum systems (OQSs) is challenging due to the intricate interplay between the system and its environment, resulting in strong quantum correlations in both space and time. This Perspective presents an overview of recently developed theoretical methods using artificial intelligence (AI) and quantum computing (QC) to simulate the dynamics of these systems. We briefly introduce the dissipaton-embedded quantum master equation in second quantization, which provides a single master equation suitable for representation by neural quantum states or quantum circuits. The promising performance of AI- and QC-based approaches is demonstrated through preliminary research on simulating the quantum dissipative dynamics of many-body OQSs. We also discuss the limitations and future developments of these methods, which hold promise for overcoming the computational challenges associated with many-body OQS dynamics.

The integration of artificial intelligence (AI) and quantum computing is poised to redefine the landscape of financial risk modeling and enterprise decision-making systems. This paper investigates the synergistic potential of these transformative technologies, emphasizing the development of hybrid AI-quantum algorithms to address the increasing complexity of modern financial systems. Traditional risk modeling methodologies often face significant limitations in capturing intricate market dynamics and accounting for real-time decision-making constraints. By leveraging quantum computing's unparalleled computational capabilities, particularly its ability to handle high-dimensional optimization problems, AI-powered quantum algorithms present a paradigm shift in financial risk prediction and mitigation. The research elaborates on key applications, including portfolio optimization, fraud detection, and credit risk analysis, demonstrating how quantum-enhanced AI algorithms achieve superior performance in terms of accuracy, efficiency, and scalability compared to classical approaches. The study begins by elucidating the theoretical underpinnings of hybrid AI-quantum systems, detailing their algorithmic structures and computational advantages. Quantum-inspired AI techniques, such as quantum neural networks and quantum-enhanced support vector machines, are examined for their ability to process vast datasets with unparalleled speed and precision. Portfolio optimization is analyzed as a case study, showcasing how quantum algorithms excel in minimizing risk while maximizing returns within a multidimensional constraint environment. Similarly, advanced fraud detection systems are explored, where hybrid models significantly improve anomaly detection rates by incorporating quantum-enhanced pattern recognition. The paper also delves into credit risk analysis, emphasizing how AI-quantum solutions can predict default probabilities with unprecedented accuracy, thereby supporting financial institutions in managing systemic risks more effectively. Despite these advancements, the integration of AI and quantum computing into financial ecosystems poses substantial challenges. The research discusses issues such as algorithmic scalability, error mitigation in quantum computations, and the resource-intensive nature of quantum hardware. Furthermore, the implementation of these technologies within the existing fintech landscape is fraught with obstacles, including interoperability with classical systems, regulatory compliance, and the high costs associated with quantum infrastructure. Addressing these challenges requires a multidisciplinary approach, combining expertise in quantum mechanics, AI, and financial engineering to develop robust, scalable solutions. The paper also examines the broader implications of AI-quantum integration for enterprise decision systems. By enabling real-time analysis of volatile markets, these technologies empower organizations to make informed, data-driven decisions, thereby enhancing operational resilience and competitive advantage. Furthermore, the ethical considerations and regulatory frameworks governing the deployment of such advanced systems are critically analyzed, emphasizing the need for transparency, fairness, and accountability in algorithmic decision-making. The findings presented in this study underscore the transformative potential of AI-powered risk modeling in quantum finance. By bridging the gap between theoretical advancements and practical implementations, this research contributes to the growing body of knowledge on hybrid AI-quantum systems and their applications in the financial domain. Ultimately, the integration of AI and quantum computing represents a pivotal development in enterprise decision systems, offering unprecedented opportunities to address the complexities of financial risk management in an increasingly interconnected and uncertain world.

The mutually beneficial interaction that exists between artificial intelligence (AI) and quantum computing inside decentralized networks is presented in this research study. This study studies the possibility that artificial intelligence algorithms could improve the distribution of entanglement in quantum networks. Entanglement distribution is an essential component for ensuring secure communication and computation in the quantum domain. Several artificial intelligence (AI) approaches, including machine learning and optimization algorithms, are utilized in this study to offer novel strategies for optimizing entanglement distribution procedures. These strategies will ultimately result in quantum communication protocols that are more efficient and trustworthy. Through the overcoming of existing hurdles and the unlocking of new possibilities for quantum information processing and communication, the combination of artificial intelligence and quantum computing has the potential to revolutionize decentralized networks for the better.

The mutually beneficial interaction that exists between artificial intelligence (AI) and quantum computing inside decentralized networks is presented in this research study. This study studies the possibility that artificial intelligence algorithms could improve the distribution of entanglement in quantum networks. Entanglement distribution is an essential component for ensuring secure communication and computation in the quantum domain. Several artificial intelligence (AI) approaches, including machine learning and optimization algorithms, are utilized in this study to offer novel strategies for optimizing entanglement distribution procedures. These strategies will ultimately result in quantum communication protocols that are more efficient and trustworthy. Through the overcoming of existing hurdles and the unlocking of new possibilities for quantum information processing and communication, the combination of artificial intelligence and quantum computing has the potential to revolutionize decentralized networks for the better.

Rapid advances in artificial intelligence (AI) and quantum computing can potentially transform cybersecurity. It explores the synergistic integration to improve cyber defenses. The ability of AI to analyze data, detect anomalies, and perform predictive analytics, combined with the potential of quantum computing to solve complex cryptographic problems, provides a strong framework for today's cybersecurity challenges. Using quantum algorithms and machine learning techniques, it aims to strengthen cryptographic methods, optimize threat detection systems, and develop flexible defense protocols against sophisticated cyber attacks. The study also examines the implications of quantum cryptography and the role of AI in managing quantum-generated data It discusses the challenges and ethical considerations associated with applying these advanced technologies in cybersecurity infrastructures. The findings suggest that the convergence of AI and quantum computing can significantly improve the effectiveness and adaptability of cybersecurity measures, paving the way for a secure digital future.

Coalition of explainable artificial intelligence and quantum computing in precision medicine

This paper will review the basic building blocks of quantum computing and discuss the main applications in artificial intelligence that can be addressed more efficiently using the quantum computers of today. Artificial intelligence and quantum computing have many features in common. Quantum computing can provide artificial intelligence and machine learning algorithms with speed of training and computational power in less price. Artificial intelligence on the other hand can provide quantum computers with the necessary error correction algorithms. Some of the algorithms in AI that have been successfully implemented on a quantum computer, that we will present in this paper, are both unsupervised learning algorithms (clustering and Principal component analysis) and also supervised learning classification, such as support vector machines.

Abstract: This research volume offers a comprehensive and academically rigorous examination of the intersection between high-dimensional artificial intelligence (AI) and quantum computing, with a focus on their combined potential to transform scientific discovery, genomic analysis, and economic modeling. The book constructs an integrated conceptual and computational framework that synthesizes tensor-based AI architectures with quantum state formalism, enabling scalable solutions to complex, multi-dimensional problems that are intractable with classical methods alone. The study begins by analyzing the theoretical underpinnings of high-dimensional data spaces, manifold learning, and quantum mechanical principles such as superposition and entanglement. From this foundation, the volume explores hybrid quantum-AI methodologies applied to dynamic systems modeling in the sciences, precision diagnostics in genomics, and macroeconomic forecasting. Methodologically, the work employs a combination of variational quantum algorithms, quantum kernel methods, and tensor factorization, supported by simulations, real-world datasets, and benchmarking. Key results highlight improvements in model efficiency, predictive accuracy, and scalability across all domains explored. In genomics, quantum-AI models enable multi-omic integration and improved variant detection; in economics, they provide enhanced risk modeling and policy simulation. The implications extend to both theory and application, offering pathways for advancing scientific inquiry, personalizing medicine, and shaping resilient economic systems. The book concludes with a critical assessment of ethical, epistemological, and governance considerations, ensuring responsible and equitable development of quantum-AI technologies. Keywords High-dimensional AI, quantum computing, quantum machine learning, tensor factorization, quantum kernel methods, genomic intelligence, economic forecasting, hybrid quantum-classical systems, quantum reinforcement learning, multi-omic data integration, macroeconomic modeling, quantum algorithms, data scalability, scientific simulation, epistemological implications, algorithmic ethics, responsible AI, quantum explainability, federated quantum learning, quantum cloud infrastructure.