This survey explores the intersection of quantum computing and fuzzy time series (FTS) forecasting. We review foundational quantum principles, fuzzy logic and FTS mathematics, hybrid quantum–fuzzy architectures, practical applications, and open research challenges. The paper provides a formal mathematical formulation of FTS, the mapping of fuzzy membership degrees into quantum amplitude vectors, proposed hybrid quantum–fuzzy inference systems (QFIS), and real equations. This survey aims to provide a comprehensive, structured, and modern view of the field for future research
This survey explores the intersection of quantum computing and fuzzy time series (FTS) forecasting. We review foundational quantum principles, fuzzy logic and FTS mathematics, hybrid quantum–fuzzy architectures, practical applications, and open research challenges. The paper provides a formal mathematical formulation of FTS, the mapping of fuzzy membership degrees into quantum amplitude vectors, proposed hybrid quantum–fuzzy inference systems (QFIS), and real equations. This survey aims to provide a comprehensive, structured, and modern view of the field for future research
Quantum Machine Learning (QML) has emerged as a groundbreaking approach within the rapidly evolving field of Quantum Computing (QC), leveraging quantum phenomena such as superposition and entanglement to surpass classical models in representational power, optimization speed, and computational efficiency. This paradigm shift paves the way for transformative applications across diverse domains while simultaneously confronting the inherent complexities of quantum mechanics and hardware limitations. Among the powerful computational models used for decision-making and pattern recognition, Fuzzy Cognitive Maps (FCMs) have demonstrated exceptional capabilities in modeling complex systems. Researchers have continually refined the traditional FCM framework by integrating novel features to enhance its effectiveness. However, despite its potential, the integration of quantum computing with FCMs remains largely unexplored, with only a limited number of studies addressing this intersection. This work provides a comprehensive theoretical algorithmic evaluation of the integration between Quantum Computing and FCMs, investigating potential advancements, challenges, and novel applications. Additionally, we present a systematic review of existing research on Quantum Fuzzy Cognitive Maps (QFCMs), offering new insights into their theoretical foundations and future directions. By bridging the gap between quantum computing and cognitive modeling, this study aims to unlock new possibilities for intelligent decision-making systems in the quantum era.
Quantum computing, grounded in the principles of superposition and entanglement, has emerged as a revolutionary paradigm with the potential to outperform classical systems in specific computational tasks. This paper presents a comprehensive survey of the foundational concepts, algorithmic developments, hardware architectures, and programming ecosystems in quantum computing. We analyze the current progress in key application areas such as cryptography, machine learning, optimization, and quantum chemistry. Furthermore, we review recent advances between 2023 and 2025, including developments in fault-tolerant architectures and quantum error correction. The paper also discusses the significant technical challenges that hinder large-scale practical deployment, including qubit scalability, decoherence, and programming limitations. By synthesizing literature across disciplines, this review aims to provide a holistic understanding of the quantum computing landscape and outline critical directions for future research and integration with classical systems.
This perspective explores the potential integration of quantum computing principles with artificial intelligence to enhance time-series forecasting. GOAL: The primary goal is to provide a structured overview of how quantum algorithms, particularly quantum reservoir computing and quantum neural networks, could improve the accuracy, efficiency, and capabilities of AI in processing and predicting temporal data. METHODS OF INQUIRY: This study is presented as a NARRATIVE REVIEW, synthesizing information from existing literature on quantum computing, AI, time-series analysis, and theoretical quantum mechanics to map the potential landscape, challenges, and future directions. IMPLICATIONS: We discuss the potential implications for the management of technology, highlighting the nascent stage of quantum AI, the significant hardware and algorithmic challenges that remain, and the critical need for robust ethical frameworks to guide development and deployment in areas like finance, climate modeling, and healthcare. The findings underscore the necessity for interdisciplinary collaboration and strategic R&D investment to navigate the complexities of this converging technological frontier.
Time series forecasting is a critical problem with applications in various fields, such as finance, energy, and environmental science. While classical machine learning methods have shown significant progress in this domain, the advent of quantum machine learning introduces a new perspective to address the complexities of time series prediction. In this research, we propose a novel Quantum Neural Network (QNN) approach for time series forecasting, leveraging the unique capabilities of quantum computing to capture intricate temporal dependencies and exploit quantum parallelism. By encoding time series data into quantum states and designing quantum circuits for processing, the QNN aims to achieve improved predictive accuracy compared to classical neural networks. Through extensive experiments on diverse time series datasets, we demonstrate the potential of our QNN framework and discuss the future prospects of quantum-powered time series prediction in real-world applications.
Quantum computing represents a transformative shift in computational paradigms by leveraging quantum mechanical phenomena such as superposition, entanglement, and quantum interference. Unlike classical computers, which rely on binary logic, quantum systems exploit these principles to solve certain problems more efficiently. This survey presents a comprehensive overview of the field, drawing from extensive literature spanning foundational quantum principles through key quantum algorithms, hardware platforms, and programming tools. The analysis bridges theoretical foundations with practical engineering challenges, examining both current capabilities and fundamental limitations of quantum systems. The paper also explores applications in cryptography, optimization, and quantum simulation, while addressing the ethical implications and long-term prospects of quantum technologies. Distinct from prior surveys, this work provides a unified perspective that connects quantum theory to practical implementation, supporting researchers and students in navigating the evolving landscape of quantum computing.
Stay ahead of the technological curve with this comprehensive, practical guide that showcases how the fusion of quantum principles and soft computing is delivering transformative solutions across finance, healthcare, and manufacturing. Quantum-Inspired Approaches for Intelligent Data Processing explores the cutting-edge fusion of quantum computing principles and soft computing techniques, unraveling the synergistic potential of these two paradigms. The book uses a comprehensive interdisciplinary approach, delving into the foundations of quantum mechanics and soft computing essentials, including fuzzy logic, genetic algorithms, and neural networks. Distinctive in its practical focus, the book showcases how this integration enhances intelligent data processing across various industries, offering tangible solutions to complex challenges. Through real-world applications, this book illuminates the transformative impact of quantum-inspired soft computing across multiple industries, from finance and healthcare to manufacturing. It incorporates case studies, examples, and market analyses, providing a holistic understanding of the subject and exploring emerging trends, challenges, and future opportunities, making it an invaluable resource for researchers and industrialists navigating the dynamic intersection of quantum computing and soft computing in intelligent data processing.