Quantum computing as emerging technology raises high expectations to accelerate complicated calculations, superseding capabilities of presently available supercomputers and to provide a solution to the ever-increasing challenge of processing big amount of data. Besides theoretical promises, quantum computers have successfully been realized in experimental settings during the past few years, and can now also be accessed commercially. Given this availability of quantum computers, their application potential can be scrutinized. Promised to lead to disruptive changes in addressing and solving problems from the domains of simulation, optimization and machine learning, quantum computers have been researched in context of many application scenarios in industrial and academic setting in the recent years. However, it turns out that currently available quantum computers are still to small in size and limited in quality to fulfill the high hopes set in them at the moment. This review reflects on the current state of the technology coming from the perspective of the application, and indicates the developments required on hardware, software and algorithmic side to eventually make quantum computing beneficial for applications.
Quantum computing as emerging technology raises high expectations to accelerate complicated calculations, superseding capabilities of presently available supercomputers and to provide a solution to the ever-increasing challenge of processing big amount of data. Besides theoretical promises, quantum computers have successfully been realized in experimental settings during the past few years, and can now also be accessed commercially. Given this availability of quantum computers, their application potential can be scrutinized. Promised to lead to disruptive changes in addressing and solving problems from the domains of simulation, optimization and machine learning, quantum computers have been researched in context of many application scenarios in industrial and academic setting in the recent years. However, it turns out that currently available quantum computers are still to small in size and limited in quality to fulfill the high hopes set in them at the moment. This review reflects on the current state of the technology coming from the perspective of the application, and indicates the developments required on hardware, software and algorithmic side to eventually make quantum computing beneficial for applications.
Quantum computing has evolved from a largely theoretical discipline into a field of growing interest for commercial and industrial applications. Recent advances in quantum hardware, algorithms, and cloud-based access have enabled early experimentation outside academic environments, raising questions about its potential impact on real-world decision-making and operational problems. This paper provides an overview of the current state of quantum computing with a focus on its emerging relevance in commercial contexts. It examines the categories of problems that have attracted attention from industry, particularly those involving high combinatorial complexity such as optimization, scheduling, logistics, and resource allocation. The analysis also reviews the global landscape of companies and initiatives engaged in quantum computing, highlighting different technological approaches, maturity levels, and regional characteristics. In parallel, the paper discusses the principal challenges and limitations that constrain widespread adoption, including hardware immaturity, algorithmic readiness, cost factors, and organizational requirements. By considering both opportunities and risks, this study aims to present a balanced and realistic perspective on the role quantum computing may play in business environments. The objective is to clarify the current position of quantum computing within the broader technological landscape and to support informed interpretation of its potential, limitations, and future trajectory in commercial applications.
It is expected that quantum computers would enable solving various problems that are beyond the capabilities of the most powerful current supercomputers, which are based on classical technologies. In the last three decades, advances in quantum computing stimulated significant interest in this field from industry, investors, media, executives, and general public. However, the understanding of this technology, its current capabilities and its potential impact in these communities is still lacking. Closing this gap requires a complete picture of how to assess quantum computing devices' performance and estimate their potential, a task made harder by the variety of quantum computing models and physical platforms. Here we review the state of the art in quantum computing, promising computational models and the most developed physical platforms. We also discuss potential applications, the requirements posed by these applications and technological pathways towards addressing these requirements. Finally, we summarize and analyze the arguments for the quantum computing market's further exponential growth. The review is written in a simple language without equations, and should be accessible to readers with no advanced background in mathematics and physics.
In the article the authors aim at presenting an overview of the current state of quantum computing, exploring qubits, gates, circuits, error correction, sample algorithms, and potential applications as well as an overview of current and near future applications. The article highlights the rapid growth in quantum computing research and argues that now is the opportune time to pursue practical applications. By delving into the fundamental aspects of quantum computing and examining the challenges and advancements in the field, the article provides a comprehensive understanding of the current landscape and emphasizes the importance of scaling and accelerating the development of quantum technologies to unlock their full potential
Abstract The rapid advancement of artificial intelligence has made machine learning (ML) methods central to modern science, technology, and industry. At the same time, the emergence of programmable quantum computers, along with the anticipation of large-scale fault-tolerant machines in the future, has generated interest in the potential of quantum machine learning (QML)—a field that explores quantum properties to enhance ML tasks. While QML is often considered a promising application for quantum computing, its practical advantages over classical methods remain an area of active research and exploration. In recent years, QML has garnered significant global attention, with researchers investigating its potential and limitations. In this chapter, we introduce the fundamentals of QML and provide an overview of recent developments and future trends in the field. We outline potential opportunities where quantum properties might offer advantages in ML tasks while also addressing the open challenges that QML faces in terms of scalability, implementation, and practical utility. We also explore the potential applications of QML in defense and security-sensitive domains. Here, researchers speculate that integrating quantum computing into ML could lead to the development of systems capable of resisting sophisticated threats, such as data manipulation and poisoning. However, realizing these possibilities requires further advancements in both quantum hardware and algorithmic design, making QML a promising yet still evolving field.
Advancements in computer technologies have been evolving in order to provide new services or to improve recent computing performance. Applications of computer technologies to several real-world domains are becoming more realistic and efficient. Two challenges are taken into consideration when applying such technologies including more complicated user requirements and exponential growth of data. In the real world, which is driven by business, achieving user requirements is one promising way of yielding competitive advantages. Moreover, time constraints are also crucial for corporate users. Apart from structured data, unstructured data have been significantly generated via several platforms. Such aspects make data processing more demanding. Quantum technology, which is instantiated in physics, has been utilized in various fields including computer science. Even the real quantum computer is still in its early development, but other concepts such as quantum information and quantum computing are making progress. Furthermore, some quantum-based algorithms have been analytically proven to have higher performance compared to the traditional ones. According to such important milestones, quantum computing has been regarded as one of the key infrastructures of future computing. This paper aims to focus on the main aspects of quantum computing including its backgrounds and recent advancement to provide a clearer picture. Its recent applications to intelligent system enhancement, cybersecurity and cryptography empowerment, and finance are addressed. Several key ideas, and opportunities of quantum computing utilization are also outlined based upon systematic reviews of existing works and case studies. Moreover, comparative analysis between classical and quantum computing in terms of core principles, computational efficiency, and practical feasibility is given. Better understanding and proper preparation of applying quantum computing are thus achieved.
Quantum computing is an emergent technology with great promise to reshape machine learning. It enables computations intractable by any classical system. This paper discusses the basic concepts of quantum computing, its advantages, and their limitations regarding ML. It can be concluded that quantum computers leverage phenomena in quantum mechanics, such as superposition, entanglement, and interference execute those computations that are beyond the capability of classical systems. A few of the most important fancies in QSVM, QPCA, and QNN perform such tasks as computing kernel matrices, high-dimensional data processing, and solving optimization problems in some key points. In contrast, several formidable challenges persist, beginning with the scalability and coherence of qubits as hardware-related issues, and extending to software-centric problems such as data encoding and the efficiency of specific algorithmic applications. This paper also underscores potential future avenues for advancement, including the development of hybrid quantum-classical algorithms, quantum error correction techniques, and quantum-native machine learning models, aimed at surmounting these limitations. By addressing these challenges and directing focus towards prospective research, quantum computing holds the promise of unlocking transformative breakthroughs in various domains such as big data processing, drug development, financial modeling, and natural language processing. Such advancements could firmly establish quantum computing as the cornerstone of next-generation artificial intelligence technologies.