This paper discusses the basic components that are required to build a quantum computing environment. Quantum computers have a distinctive advantage over classical computers due to its ability to solve problems with large number of computations faster. To utilize these capabilities to its best, we should ensure that the computers are on par with the quantum computing requirements to float a working environment. The whole thrust on this is to ensure that the quantum computing environment is chained by the laws of quantum mechanics. The issue becomes more complicated when it is to be executed on a classical platform. This paper surveys the basics of Quantum Computing and the existing Quantum Computing simulators. Further, it also points out the basic rules to ensure a proper translation of each capabilities from a classical system to a quantum system and vice versa.
This paper discusses the basic components that are required to build a quantum computing environment. Quantum computers have a distinctive advantage over classical computers due to its ability to solve problems with large number of computations faster. To utilize these capabilities to its best, we should ensure that the computers are on par with the quantum computing requirements to float a working environment. The whole thrust on this is to ensure that the quantum computing environment is chained by the laws of quantum mechanics. The issue becomes more complicated when it is to be executed on a classical platform. This paper surveys the basics of Quantum Computing and the existing Quantum Computing simulators. Further, it also points out the basic rules to ensure a proper translation of each capabilities from a classical system to a quantum system and vice versa.
Quantum computing has the potential to provide exponential performance benefits in processing over classical computing. It utilizes quantum mechanics phenomena (such as superposition, entanglement, and interference) to solve a computational problem. It can explore atypical patterns over data that classical computers can't perform efficiently. Quantum computers are in the nascent stage of development and are noisy due to decoherence, i.e., quantum bits deteriorate with environmental interactions. It will take a long time for quantum computers to achieve fault tolerance although quantum algorithms can be developed in advance. Heavy investment in developing quantum hardware, software development kits, and simulators has led to multiplicity of quantum development tools. Selection of a suitable development platform requires a proper understanding of the capabilities and limitations of these tools. Although a comprehensive comparison of the different quantum development tools would be of great value, to the best of our knowledge, no such extensive study is currently available.
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.
Quantum computing is necessary to be described as a powerful tool is the new generation of computational power that works based on the principles of quantum mechanics, allowing the solving of problems that are intractable for classical computers. Due to the utilization of such phenomena as superposition, quantum entanglement and quantum interference, quantum computers are said to be able to solve problems in such areas as cryptography, artificial intelligence, optimization problems and material sciences, among others. This paper attempts to examine the basic concepts of quantum computing while focusing on their different aspects and their potential impacts. This study its experience and potential in its current form and in future applications through examples, illustrations, and theoretical discussion. Specific problems, including error correction, hardware scalability, and requirements for resources to address, make the paper a comprehensive overview of the quantum computing potential and weaknesses. Moving towards practical examples, the discussion covers all aspects of the book, providing readers with a holistic understanding of how quantum computing is going to change technological paradigms in the forthcoming decades.
Despite the significant technological advancements, the amount of data to be processed remains enormous, and certain problems remain unsolved or take millions of years to be solved. The advent of quantum computing, with its unique properties, represents a pivotal point in the technological world. Although quantum computers are still under development in the laboratories, research is progressing in parallel with the aim of creating a good basis for any contribution in order to be ready for the commercialization of the first computer. This article clarifies the essential basics required before entering the quantum realm. We present the basic properties of quantum computing based on four fondations and their differences from classical computing.
By using the quantum mechanics phenomenon, quantum computers provide a new dimension of computational power that drastically accelerates solving complex and resource-intensive problems. One of the most evolving but, due to the computation power of standard computers, reasonably limited application domain is natural language processing (NLP). NLP seeks to give interactive systems the ability to understand and manipulate human language. Making systems understand and manipulate human languages requires large amounts of data and computational power during learning as well as during the execution of NLP. For handling these amounts of data like text and audio recordings and the complexity of classical NLP algorithms, quantum computation has emerged as a promising solution. This work gives an extensive overview of this new field, known as quantum natural language processing (QNLP). Introducing the basics of quantum computing, we discuss its use in NLP by explaining the different proposed embedding models, quantum algorithms, and other methods of QNLP. As QNLP is still in its infancy, this comprehensive overview is the foundation that points to the upcoming research direction.
Quantum machine learning has become a promising field due to the capability to solve the computational issues of classical machine learning models. This research investigates the integration of machine learning and quantum computing with the aim of establishing and utilizing the QML models including QSVM and QNN. Given the large number of experiments on both conventional and quantum enriched datasets, the authors show that quantum machine learning models can be faster and more accurate when compared to Classical models. Furthermore, they provide a practical way for extending current quantum models’ capabilities to fight against available quantum device limitations, achieve higher computation rates and better data handling. Savings in training times and better performance are well-demonstrated in the outcome – especially with large data sets like MNIST. As much as such issues as quantum hardware constraints persist, marrying quantum computing with machine learning expands the ability to solve problems that are big and complicated across several fields including finance, healthcare and AI. This paper therefore encourages more investment on the you need more research and development towards the realization of quantum computing in the realm of machine learning.