پژوهشگاه علوم و فناوری اطلاعات ایران (ایرانداک) پژوهشنامه پردازش و مدیریت اطلاعات 2251-8223 40 ویژه نامه انگلیسی 4 2025 07 23 Harnessing Quantum Computing for Real-Time Data Analytics: A 2025 Perspective Harnessing Quantum Computing for Real-Time Data Analytics: A 2025 Perspective 315 340 728117 10.22034/jipm.2025.728117 FA Ahmed Ali Hussein Al-Turath University, Baghdad 10013, Iraq 0009-0006-2664-341X Abdul Monem S. Rahma Al-Mansour University College, Baghdad 10067, Iraq 0000-0001-6323-9148 Arapbaev Ruslanbek Nurmamatovich Osh State University, Osh City 723500, Kyrgyzstan 0000-0003-3157-9716 Rana Khudhair Abbas Ahmed Al-Rafidain University College Baghdad 10064, Iraq 0000-0002-3864-1669 Faris Abdul Kareem Khazal Madenat Alelem University College, Baghdad 10006, Iraq 0000-0001-7873-2461 Journal Article 2025 08 03 <strong>ABSTRACT</strong><br /><strong> </strong><br /><strong>Background: </strong>Quantum computing has brought in all new paradigm for computational processing providing unparallel ability for data analysis. Considering worldwide data production is expected to exceed 180 trillion zettabytes by 2025 the utilization of the conventional computing framework hampers the real-time processing of data. People consider quantum computing, which uses principles of quantum mechanics to solve problems 100 and 1,000 times faster than classical computing.<br /><strong> </strong><br /><strong>Objective: </strong>The article looks at quantum computing and its relevance to real time data analytics to determine its relevance, hence its impact, by the year 2025. It is worthwhile to emphasize the comparison of quantum algorithms with traditional approaches to dealing with extensive, data-centered workloads in various fields.<br /><strong> </strong><br /><strong>Methods: </strong>A comparison was made on quantum versus classical computing algorithms based on criteria such as, the flow rate, precision, and flexibility. Data sets provided by the finance stream, including real-time stock analysis, supply chain and logistics, genomic sequencing from the healthcare domain were used. Over 10 million simulation experiments were performed to gain trends and insights into the operational problems for quantum simulation.<br /><strong> </strong><br /><strong>Results: </strong>The study establishes differences in the efficiencies of these two approaches, with quantum algorithms speeding up particular tasks as much as a hundred times higher than classical algorithms and almost 15% of the error rate being decreased if quantum error correction modes were used. In scalability tests it was shown that quantum systems could process data sets larger than 10 terabytes with little slowdown, compared to a classical system, which reduced efficiency by as much as 30%. However, in present day quantum hardware, processing the capability is limited and problems arise with regards the error correction protocol.<br /><strong> </strong><br /><strong>Conclusion: </strong>Quantum computing, on the other hand, has an unconventional prospect of real-time data analytics to operate at high efficiency and big scale on data-bound concerns. However, much progress is required in the way of bettering coherence times and reducing exacting error rates, crucial advances for total realization of quantum potentialities by 2025 <strong>ABSTRACT</strong><br /><strong> </strong><br /><strong>Background: </strong>Quantum computing has brought in all new paradigm for computational processing providing unparallel ability for data analysis. Considering worldwide data production is expected to exceed 180 trillion zettabytes by 2025 the utilization of the conventional computing framework hampers the real-time processing of data. People consider quantum computing, which uses principles of quantum mechanics to solve problems 100 and 1,000 times faster than classical computing.<br /><strong> </strong><br /><strong>Objective: </strong>The article looks at quantum computing and its relevance to real time data analytics to determine its relevance, hence its impact, by the year 2025. It is worthwhile to emphasize the comparison of quantum algorithms with traditional approaches to dealing with extensive, data-centered workloads in various fields.<br /><strong> </strong><br /><strong>Methods: </strong>A comparison was made on quantum versus classical computing algorithms based on criteria such as, the flow rate, precision, and flexibility. Data sets provided by the finance stream, including real-time stock analysis, supply chain and logistics, genomic sequencing from the healthcare domain were used. Over 10 million simulation experiments were performed to gain trends and insights into the operational problems for quantum simulation.<br /><strong> </strong><br /><strong>Results: </strong>The study establishes differences in the efficiencies of these two approaches, with quantum algorithms speeding up particular tasks as much as a hundred times higher than classical algorithms and almost 15% of the error rate being decreased if quantum error correction modes were used. In scalability tests it was shown that quantum systems could process data sets larger than 10 terabytes with little slowdown, compared to a classical system, which reduced efficiency by as much as 30%. However, in present day quantum hardware, processing the capability is limited and problems arise with regards the error correction protocol.<br /><strong> </strong><br /><strong>Conclusion: </strong>Quantum computing, on the other hand, has an unconventional prospect of real-time data analytics to operate at high efficiency and big scale on data-bound concerns. However, much progress is required in the way of bettering coherence times and reducing exacting error rates, crucial advances for total realization of quantum potentialities by 2025 https://jipm.irandoc.ac.ir/article_728117_c0b232155ba90b94f37149909b7e6459.pdf