Where quantum computing is emerging as a catalyst for innovation, this paper investigates its influence on R&D efficiency and innovation outcomes across 4,500 firms in six key sectors – pharmaceuticals, finance, materials science, energy, telecommunications, and logistics – in Asia between 2015 and 2024. The study employs advanced models, including Agent-Based Modeling (ABM), Bayesian Networks, Support Vector Machines (SVM), and Markov Chains, to analyse the relationship between quantum computing investments and innovation performance.The results indicate that high-R&D sectors, such as pharmaceuticals and finance, benefit most from quantum computing due to its role in accelerating drug discovery and optimising risk management. In contrast, low-R&D sectors like telecommunications and logistics face slower adoption due to infrastructure challenges and limited resources. Government support, particularly through subsidies and tax incentives, enhances the impact of quantum computing investments on innovation outcomes, especially in high-R&D industries. This study addresses key gaps by providing empirical evidence of quantum computing’s role in driving sectoral innovation and offering actionable insights for policymakers and industry leaders.
Where quantum computing is emerging as a catalyst for innovation, this paper investigates its influence on R&D efficiency and innovation outcomes across 4,500 firms in six key sectors – pharmaceuticals, finance, materials science, energy, telecommunications, and logistics – in Asia between 2015 and 2024. The study employs advanced models, including Agent-Based Modeling (ABM), Bayesian Networks, Support Vector Machines (SVM), and Markov Chains, to analyse the relationship between quantum computing investments and innovation performance.The results indicate that high-R&D sectors, such as pharmaceuticals and finance, benefit most from quantum computing due to its role in accelerating drug discovery and optimising risk management. In contrast, low-R&D sectors like telecommunications and logistics face slower adoption due to infrastructure challenges and limited resources. Government support, particularly through subsidies and tax incentives, enhances the impact of quantum computing investments on innovation outcomes, especially in high-R&D industries. This study addresses key gaps by providing empirical evidence of quantum computing’s role in driving sectoral innovation and offering actionable insights for policymakers and industry leaders.
Quantum computing is emerging as a revolutionary field with the potential to redefine modern technology. Unlike classical computers, which rely on binary computation, quantum computers leverage quantum bits (qubits) to perform complex calculations at unprecedented speeds. This paper explores the transformative impact of quantum computing across various technological domains, including cryptography, artificial intelligence, data processing, and optimization problems. In cryptography, quantum algorithms such as Shor’s algorithm pose challenges to traditional encryption methods, necessitating the development of quantum-resistant cryptographic techniques. In artificial intelligence and machine learning, quantum computing enhances pattern recognition and accelerates data analysis. Furthermore, industries such as pharmaceuticals, material science, and logistics benefit from quantum-driven advancements in molecular simulations, material design, and supply chain optimization. Despite its immense potential, challenges such as hardware limitations, error correction, and high implementation costs hinder widespread adoption. However, continuous research and advancements in quantum algorithms, superconducting qubits, and quantum networking are expected to overcome these barriers, paving the way for a new era of technological innovation.
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.
Quantum computing is emerging as a transformative general-purpose technology with the potential to redefine entrepreneurial innovation across data-intensive and optimization-driven industries. By exploiting quantum mechanical principles such as superposition, entanglement, and quantum parallelism, quantum computers offer new computational capabilities that may surpass classical systems in solving complex problems. This paper examines quantum computing as a catalyst for entrepreneurial innovation, focusing on the strategic opportunities, market disruptions, and ethical challenges associated with quantum-enabled business models in finance, supply chain optimization, and healthcare startups. Through a cross-sectoral analysis, the study explores how startups can leverage hybrid quantum–classical approaches to accelerate decision-making, enhance predictive accuracy, and unlock novel value propositions. At the same time, it addresses critical concerns related to technological uncertainty, unequal access, data security, and responsible innovation. The paper argues that while quantum computing presents significant disruptive potential, its sustainable integration into entrepreneurial ecosystems requires strategic foresight and ethical governance frameworks alongside technological adoption.
Quantum computing is emerging as one of the most transformative technologies of the 21st century, with significant implications for business strategy, workforce development, education systems, and global economic competitiveness. This paper introduces the foundational principles of quantum computing, including superposition, entanglement, interference, and decoherence, and explains how these mechanisms enable quantum systems to outperform classical computers in solving complex problems. It further examines the potential applications of quantum computing across industries such as pharmaceuticals, finance, automotive engineering, artificial intelligence, and logistics, highlighting both opportunities for innovation and risks of disruption. Attention is given to anticipated labor market shifts, the growing need for quantum literacy in higher education and professional training, and the relationship between quantum technologies and artificial intelligence acceleration. The paper also explores broader societal and demographic implications, including the risk of widening technological inequality between developed and developing nations. Ultimately, the study argues that while quantum computing presents unprecedented opportunities for economic advancement and scientific discovery, proactive policy planning, educational adaptation, and international collaboration will be essential to ensure inclusive and sustainable adoption.
Quantum computing is a vital research field in science and technology. One of the fundamental questions hardly known is how quantum computing research is developing to support scientific advances and the evolution of path-breaking technologies for economic, industrial, and social change. This study confronts the question here by applying methods of computational scientometrics for publication analyses to explain the structure and evolution of quantum computing research and technologies over a 30-year period. Results reveal that the evolution of quantum computing from 1990 to 2020 has a considerable average increase of connectivity in the network (growth of degree centrality measure), a moderate increase of the average influence of nodes on the flow between nodes (little growth of betweenness centrality measure), and a little reduction of the easiest access of each node to all other nodes (closeness centrality measure). This evolutionary dynamics is due to the increase in size and complexity of the network in quantum computing research over time. This study also suggests that the network of quantum computing has a transition from hardware to software research that supports accelerated evolution of technological pathways in quantum image processing, quantum machine learning, and quantum sensors. Theoretical implications of this study show the morphological evolution of the network in quantum computing from a symmetric to an asymmetric shape driven by new inter-related research fields and emerging technological trajectories. Findings here suggest best practices of innovation management based on R&D investments in new technological directions of quantum computing having a high potential for growth and impact in science and markets.
The convergence between artificial intelligence (AI) and quantum computing has global economic and security implications, with applications ranging from advancing pharmaceuticals to breaking traditional encryption. In its national and local state plans, China's policymakers clearly seek global leadership in both of these domains, and encourage synergies between them. Traditional research institutions, namely universities and laboratories, are leading theoretical work and basic research and development for quantum computers to power AI applications, quantum machine learning (ML), and other foundational work. Additionally, China's premier cloud service providers are advancing the two technologies with quantum simulators and online platforms for ML and algorithm development. These companies are driving the commercialization of quantum technologies for industries like pharmaceuticals, materials science, and chemistry. China's investments and rapid advancements warrant analytic attention to avoid technological surprise and inform U.S. policymakers and leaders in these fields. To that end, this chapter identifies key entities pursuing research on the nexus between the two technologies, assesses technology paths and obstacles, and provides indicators for future analyses.