Moore's law, which has helped to explain the exponential growth achieved in traditional computational power over the past half-century, has been eschewed for Neven's law. Neven's law posits that quantum computing is advancing at a doubly exponential rate. Quantum computers possess the processing power required to analyze the incalculable amount of “big data” in existence today. Together, big data and quantum computing are driving advancements in machine learning, which in turn, helps to power artificial intelligence (AI). AI, computational power, and big data will accelerate the development of technologies that will alter how humans behave and communicate. Using these three technologies as a foundation, this chapter explores (i) the explosion and analysis of “big data,” (ii) the evolution of advanced AI, and (iii) the emergence of extended reality (XR). The impact of these technologies and trends on organizations, crisis communication theory and practice, and society at-large are discussed and supported with real-world examples.
Moore's law, which has helped to explain the exponential growth achieved in traditional computational power over the past half-century, has been eschewed for Neven's law. Neven's law posits that quantum computing is advancing at a doubly exponential rate. Quantum computers possess the processing power required to analyze the incalculable amount of “big data” in existence today. Together, big data and quantum computing are driving advancements in machine learning, which in turn, helps to power artificial intelligence (AI). AI, computational power, and big data will accelerate the development of technologies that will alter how humans behave and communicate. Using these three technologies as a foundation, this chapter explores (i) the explosion and analysis of “big data,” (ii) the evolution of advanced AI, and (iii) the emergence of extended reality (XR). The impact of these technologies and trends on organizations, crisis communication theory and practice, and society at-large are discussed and supported with real-world examples.
Emerging technologies, including quantum information science and artificial education systems, have the potential to have significant implications for the future of human civilization. Quantum information, on the one hand, and machine learning (ML) and artificial intelligence (AI), on the other, consume their personal unique set of queries and contests that have been studied in isolation up until now. However, a recent study is starting to examine whether these disciplines can teach one another anything useful. The discipline of quantum ML investigates how quantum computing and ML may work together to find solutions to challenges in both areas. Major advancements in the two areas of effect have been made recently. Particularly relevant in today's “big data” era is the use of quantum computing to speed up the solution of machine learning (ML) challenges. However, ML is already present in many state-of-the-art technologies and may play a crucial role in future quantum technologies.
Fast data and growing artificial intelligence have brought traditional computer infrastructures to their limits. They expose inherent limitations in processing speed, energy efficiency, and scalability. In the face of nearing physical barriers with Moore's Law and memory bottlenecks plaguing traditional von Neumann architectures, quantum computing offers a completely new way of leveraging principles such as superposition and entanglement to redefine computation possibilities. This review is concerned with addressing how quantum computing stands to do that foundational AI and big data challenges, from accelerating extremely complex optimization tasks to enabling very secure, real-time analytics. These topics include basic principles of quantum mechanics, the basis of qubits and quantum circuits; updates in quantum algorithms (such as Grover's search, quantum annealing); and hybrid classical-quantum systems that serve as pathways through technological gaps. The paper suggests major applications of transformative nature in some of the higher-impact domains: quantum-accelerated drug discovery for health, portfolio optimization in finance, and quantum-secured communication infrastructures to guard the IoT. Case studies present early successes on quantum machine learning models improving diagnostic accuracy and annealing-based traffic optimization for urban congestion. Decoherence, error-prone devices, as well as low software ecosystems are conspiring against the actual roadways to practical quantum advantages. Future progress is dependent on the innovative development of error-corrected qubits, scalable algorithms, as well as interdisciplinary collaboration. While universal quantum supremacy remains while unreached, this coming decade promises a series of developments toward applied, specific applications that will place quantum computing as an added force among the classical systems. This review tries to project both sides of the likely potential of the space, obstacles, and path, paving the way for the researcher to find their foot in the continually progressing quantum-AI terrain.
Emerging technologies like quantum computing and Artificial Intelligence (AI) are radically changing how we approach computing technology. The fusion of these disciplines within cloud systems offers incredible computing power, which boosts effectiveness and flexibility. Productivity in almost every area – be it optimization, data processing, or machine learning – undergoes a significant transformation. This chapter offers an in-depth study of quantum AI in the scope of the cloud with respect to its basics, sophisticated integration frameworks, and advanced-level practical applications. It also outlines the limits of this new domain, such as hardware and software development, and other secondary issues like safety defects. In addition, this chapter addresses the most notable cloud-based platforms for quantum computing and their game-changing impact on the acceleration of AI development and implementation that will define the future of intelligent computing.
The rapid accumulation of big data in the Internet era has gradually decelerated the progress of Artificial Intelligence (AI). As Moore's Law approaches its limit, it is imperative to break the constraints that are holding back artificial intelligence. Quantum computing and artificial intelligence have been advancing along the highway of human civilization for many years, emerging as new engines driving economic and social development. This article delves into the integration of quantum computing and artificial intelligence in both research and application. It introduces the capabilities of both universal quantum computers and special-purpose quantum computers that leverage quantum effects. The discussion further explores how quantum computing enhances classical artificial intelligence from four perspectives: quantum supervised learning, quantum unsupervised learning, quantum reinforcement learning, and quantum deep learning. In an effort to address the limitations of smart cities, this article explores the formidable potential of quantum artificial intelligence in the realm of smart cities. It does so by examining aspects such as intelligent transportation, urban operation assurance, urban planning, and information communication, showcasing a plethora of practical achievements in the process. In the foreseeable future, Quantum Artificial Intelligence (QAI) is poised to bring about revolutionary development to smart cities. The urgency lies in developing quantum artificial intelligence algorithms that are compatible with quantum computers, constructing an efficient, stable, and adaptive hybrid computing architecture that integrates quantum and classical computing, preparing quantum data as needed, and advancing controllable qubit hardware equipment to meet actual demands. The ultimate goal is to shape the next generation of artificial intelligence that possesses common sense cognitive abilities, robustness, excellent generalization capabilities, and interpretability.
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 exponential growth of big data in artificial intelligence (AI) has posed significant challenges for scaling machine learning (ML) models efficiently. Classical computing methods struggle to process vast datasets and optimize complex ML models in a feasible timeframe. Quantum computing offers a promising paradigm shift by leveraging quantum mechanics to perform computations at unprecedented scales and speeds. This study explores the role of quantum computing in addressing the big data problem in AI, with a focus on its potential to enhance the scalability of ML models. Key topics include quantum-enhanced data processing, optimization of large-scale algorithms, and advancements in quantum machine learning (QML). We analyze the theoretical benefits of quantum approaches, such as faster matrix inversion and efficient kernel methods, alongside practical developments in quantum hardware and hybrid classical-quantum frameworks. The study further highlights the challenges, such as noise, hardware limitations, and the gap between theoretical and practical implementations. By bridging quantum computing with AI, this research identifies avenues for accelerating innovation in scalable ML, paving the way for breakthroughs in data-intensive applications. KEYWORDS: Quantum computing, artificial intelligence, big data, machine learning, scalability, quantum machine learning (QML), data processing, optimization, hybrid quantum-classical frameworks