INTRODUCTION: Quantum computing technology has become a center of attention in various scientific disciplines, including economic analysis. The adoption of quantum computing in economic analysis offers tremendous potential to improve the processing of complex economic data and provide deep insights. However, the use of quantum technology in the context of distributed information systems also raises several challenges, including data security and the limitations of quantum technology. OBJECTIVE: This research aims to investigate the implications of adopting quantum computing in economic analysis, with a focus on distributed information systems. METHODS: This research was carried out using a descriptive qualitative approach, with data derived from the results of relevant research and previous studies. The collected data will be processed and analyzed to gain a deeper understanding of the adoption of quantum computing in economic analysis in distributed information systems. RESULTS: This research then finds that the adoption of quantum computing in economic analysis has the potential to increase efficiency, accuracy, and depth of economic insight. However, limitations of current quantum technologies, including quantum errors, limited scale of operations, and data security issues, limit their applications. In the long term, research and development will be key to overcoming these obstacles and maximizing the potential of this technology in economic analysis. CONCLUSION: The long-term implications include increased economic competitiveness and significant changes in the way economic decision-making is carried out, assuming that ethical and regulatory issues are also carefully considered.
INTRODUCTION: Quantum computing technology has become a center of attention in various scientific disciplines, including economic analysis. The adoption of quantum computing in economic analysis offers tremendous potential to improve the processing of complex economic data and provide deep insights. However, the use of quantum technology in the context of distributed information systems also raises several challenges, including data security and the limitations of quantum technology. OBJECTIVE: This research aims to investigate the implications of adopting quantum computing in economic analysis, with a focus on distributed information systems. METHODS: This research was carried out using a descriptive qualitative approach, with data derived from the results of relevant research and previous studies. The collected data will be processed and analyzed to gain a deeper understanding of the adoption of quantum computing in economic analysis in distributed information systems. RESULTS: This research then finds that the adoption of quantum computing in economic analysis has the potential to increase efficiency, accuracy, and depth of economic insight. However, limitations of current quantum technologies, including quantum errors, limited scale of operations, and data security issues, limit their applications. In the long term, research and development will be key to overcoming these obstacles and maximizing the potential of this technology in economic analysis. CONCLUSION: The long-term implications include increased economic competitiveness and significant changes in the way economic decision-making is carried out, assuming that ethical and regulatory issues are also carefully considered.
The aim of this research is to investigate the effects of quantum computing on financial analysis and forecast accuracy. Given the significant increase in the volume of financial data and the complexity of financial decision-making, the need to use new technologies such as quantum computing is increasingly felt. The research collected data from 100 financial analysts, researchers, and IT professionals, as well as conducted in-depth interviews with twenty experts. The findings show that 70% of respondents emphasize that quantum computing can effectively increase the accuracy of financial forecasts. However, significant challenges have been identified, such as high costs and the need for complex infrastructure to implement the technology. Additionally, 35% of professionals express concerns about data security and privacy. Finally, this paper offers recommendations that include investing in human resource development and establishing new security protocols to best exploit the potential opportunities of quantum computing. This research can help better understand the impact of quantum computing innovations on the future of the financial industry and provide practical solutions to address emerging challenges. Also, more detailed studies on the social and economic impacts of this technology on financial markets and how it interacts with other technologies could enrich the results of this research.
Abstract Currently, E-commerce is widely adopted as it is important for business management and economic growth in the new global economy, and to reach the rapid increasing population. To better manage the e-commerce, it is important to collect and evaluate the Consumer Behaviour data for decision making and optimization. The conventional computing technologies need high amount of power and time for large data analysis. Quantum computing has the potential to analyze the large amount of data more efficiently than classical computing. This paper aims to explore the core process areas that need to be consider by the practitioners for adopting quantum computing in social business. To address the objective of this study, we conducted a literature review and empirical study to explore the core process areas that need to be considered for the consideration of quantum computing in social business. The results show that limited scalability, regulatory challenges, high initial cost, limited availability of quantum resources, education and training and security are the most important process areas that need to be focused by the industry practitioners. The result of this study gives a knowledge base for research and practitioners community to develop the tools and strategies for the adoption of quantum computing in social business.
This study provides a comprehensive review of the advancements, challenges, and potential implications of quantum computing in the field of big data analytics. The primary objective is to assess how quantum computing paradigms are transforming data processing and analysis, with a focus on their application across various sectors, including healthcare, finance, and scientific research. Employing a systematic literature review and content analysis, the study analyzes peer-reviewed articles, conference proceedings, and academic journals from databases such as PubMed, IEEE Xplore, and ScienceDirect. Key findings reveal that quantum computing, with its advanced algorithms and machine learning techniques, offers significant improvements in computational speed and efficiency over classical computing methods. This technological advancement enables the handling of large and complex datasets, presenting new opportunities in data analytics. However, the study also identifies challenges such as scalability, error correction, and integration with existing systems, which currently limit the full potential of quantum computing in big data analytics. The study concludes with strategic recommendations for industry leaders and policymakers, emphasizing the need for investment in research and development, the establishment of regulatory frameworks, and the development of educational programs to support this emerging field. Future research directions are suggested, focusing on overcoming technological limitations and exploring the long-term implications of quantum computing in various industries. This study contributes valuable insights into the evolving landscape of quantum computing and its significant impact on big data analytics. Keywords: Quantum Computing, Big Data Analytics, Advanced Algorithms, Data Processing.
Quantum computing represents a paradigm shift in information technology with the potential to revolutionize information systems by offering unprecedented computational power. This study explores the role of quantum computing in transforming information systems through a qualitative approach, utilizing literature review and library research methodologies. The research investigates how quantum computing could address complex computational challenges that are currently beyond the capabilities of classical computers, including problems related to optimization, cryptography, and large-scale data analysis. The study also examines the opportunities that quantum computing presents, such as accelerated problem-solving and enhanced data security, alongside the challenges, including technical hurdles, high costs, and the need for new algorithms and hardware. By synthesizing insights from existing academic and industry sources, this paper provides a comprehensive overview of the current state of quantum computing, its potential impact on information systems, and the critical areas that require further research and development. The findings underscore the transformative potential of quantum computing while highlighting the significant obstacles that must be overcome to fully realize its benefits. This research contributes to a deeper understanding of how quantum computing could shape the future of information systems and informs stakeholders about the strategic considerations necessary for navigating this emerging technology.
Quantum computing represents one of the most disruptive technological paradigms of the 21st century, with profound implications for economic modelling, financial optimization, and computational decision-making. Unlike classical computing, quantum systems leverage superposition, entanglement, and interference to solve problems of exponentially higher complexity, a capability with transformative potential for economics and finance. This study examines the intersection of quantum computing and economics, systematically evaluating both the promises and the barriers defining this emerging field. Regarding opportunities, quantum algorithms, such as the Quantum Approximate Optimization Algorithm (QAOA) and Grover's algorithm, demonstrate significant theoretical advantages in portfolio optimization, risk assessment, option pricing, and the resolution of large-scale combinatorial problems that are intractable for classical machines. Quantum machine learning extends these prospects, offering accelerated capabilities for pattern recognition and predictive modelling relevant to financial markets. However, substantial barriers temper these expectations. Current quantum hardware operates in the NISQ (Noisy Intermediate-Scale Quantum) era, where decoherence, gate errors, and a limited number of qubits constrain practical applicability. The gap between theoretical quantum advantage and real-world economic implementation remains significant, compounded by a shortage of specialists, high infrastructure costs, and the absence of standardized programming frameworks tailored to economic applications. Through a bibliometric analysis of existing literature and a critical review of recent experimental results, the study maps the current state of quantum computing adoption in economics, identifies the most active research frontiers—quantum game theory and quantum portfolio optimization—and outlines the conditions under which quantum advantage could become economically relevant. The findings suggest that, although quantum computing will not transform economic practice in the short term, its long-term disruptive potential warrants proactive engagement from economists, financial institutions, and policymakers.
Quantum computing represents a transformative leap in information processing, holding the promise to revolutionize industries through its unprecedented computational capabilities. This paper explores the evolving landscape of quantum computing by analyzing current developments, technological challenges, and emerging opportunities. It examines how quantum principles are being translated into real-world applications and identifies key obstacles to mainstream adoption, such as hardware instability, error correction, and the need for specialized quantum algorithms. By evaluating both the scientific and socio-economic implications, this paper aims to provide a comprehensive understanding of the future trajectory of quantum computing.