With the rapid advancement of quantum computing technology, there is a strong motivation to explore suitable practical applications for quantum algorithms and quantum computers. This chapter focuses on reviewing recent research on practical applications of quantum computing, specifically dimension reduction, pattern recognition, quantum sorting, and quantum communications for which optimized/modified versions of the quantum wavelet transform (QWT) and Grover’s algorithm are presented. For practical implementations of the presented algorithms, this chapter also includes methodologies for classical-to-quantum (C2Q) data encoding and quantum-to-classical (Q2C) data decoding. Additionally, the chapter presents an efficient quantum sorting technique by combining perfect-shuffle and bitonic networks. In the domain of quantum communications, the chapter reviews a technique that combines chaotic communications with quantum key distribution (QKD) to enhance both security and communication range. The effectiveness of these techniques is validated through practical results obtained from experiments conducted on IBM-Q simulators and hardware, as well as on high-performance-reconfigurable-computers (HPRCs). This chapter aims to provide readers with a comprehensive understanding of these applications, covering the necessary prerequisites by showcasing the potential of quantum computing in various domains for innovative problem-solving.
With the rapid advancement of quantum computing technology, there is a strong motivation to explore suitable practical applications for quantum algorithms and quantum computers. This chapter focuses on reviewing recent research on practical applications of quantum computing, specifically dimension reduction, pattern recognition, quantum sorting, and quantum communications for which optimized/modified versions of the quantum wavelet transform (QWT) and Grover’s algorithm are presented. For practical implementations of the presented algorithms, this chapter also includes methodologies for classical-to-quantum (C2Q) data encoding and quantum-to-classical (Q2C) data decoding. Additionally, the chapter presents an efficient quantum sorting technique by combining perfect-shuffle and bitonic networks. In the domain of quantum communications, the chapter reviews a technique that combines chaotic communications with quantum key distribution (QKD) to enhance both security and communication range. The effectiveness of these techniques is validated through practical results obtained from experiments conducted on IBM-Q simulators and hardware, as well as on high-performance-reconfigurable-computers (HPRCs). This chapter aims to provide readers with a comprehensive understanding of these applications, covering the necessary prerequisites by showcasing the potential of quantum computing in various domains for innovative problem-solving.
Quantum computing represents a paradigm shift in computational capabilities, promising to solve problems that are intractable for classical computers. This paper explores the architecture, implementation, and operational aspects of quantum computing, focusing on its applications in various fields. I conducted a comprehensive literature review to identify existing research gaps and propose methodologies for effective quantum computing implementation. My findings highlight the potential of quantum computing in optimization, cryptography, and machine learning, among other areas. This research paper provides a comprehensive examination of the architecture, implementation, and operational strategies of quantum computing, with a focus on its diverse applications in optimization, cryptography, and machine learning. Through an extensive literature review, I identify critical research gaps and propose innovative methodologies for effective quantum computing implementation. I conclude with strategic recommendations for future research directions, emphasizing the importance of enhancing quantum hardware, developing hybrid algorithms, and fostering interdisciplinary collaborations. This foundational study not only deepens the understanding of quantum computing’s current state but also paves the way for future advancements in this rapidly evolving field. The insights gained from the literature review, methodology, and results sections contribute to a deeper understanding of the current state of quantum computing and its potential applications.
With the promising advancement in quantum computing technology in the last decade, there is a strong motivation to find suitable applications for quantum algorithms and quantum computers. Domains such as High Energy Physics (HEP) have an enormous readout count of high-resolution data. Performing pattern recognition on this readout is computationally challenging and time-consuming because of the multi-dimensionality of the data. In this paper, we propose a methodology that employs quantum algorithms such as Quantum Wavelet Transform and Grover's search algorithm for timeefficient pattern recognition in data sets that are characterized by high spatial resolution and high dimensionality. The motivation behind using quantum algorithms is the potential speedup relative to classical methods, when performed by a quantum computer. In our proposed methodology, Quantum Wavelet Transform is performed on the high spatial resolution data to reduce its dimensionality while quantum Grover's search algorithm is employed to search for target patterns in the reduced data set. Performing the search operation on data with reduced spatial resolution, minimizes processing overheads and computation times. Moreover, use of quantum techniques yield faster results, compared to classical dimension reduction and search methods. We demonstrate the feasibility of the proposed methodology by emulating the quantum algorithms on classical hardware based on field programmable gate arrays (FPGAs). A high performance reconfigurable computer (HPRC) was used for the experimental evaluation. The obtained results are favorable towards our proposed approach.
Utilising the ideas of quantum physics to address challenging issues beyond the capacity of traditional computers, quantum calculating has emerged as a new paradigm in computing. This chapter highlights the special qualities and benefits that this technology offers while examining the enormous potential of quantum computing applications across multiple areas. Quantum computers have the potential to process computationally demanding tasks at a rate that has never been seen before by utilising entanglement and superposition, two examples of quantum phenomena. The practical applications of quantum computing are explored in this chapter, including those in the areas of scientific computing, quantum simulation, optimisation, cryptography, machine learning, and finance. It sheds light on how quantum algorithms might transform these fields and lead to advances in risk assessment, secure communication, pattern recognition, medication development, and logistics optimisation. Furthermore, the chapter also highlights new applications in communication networks, aircraft, and quantum sensing, demonstrating the technology’s broad reach. The chapter covers the obstacles and constraints that exist today as well as the prospects for the future and possible discoveries that can hasten the uptake and significance of applications utilising quantum computing.
The emergence of quantum computers in the last decade has generated research interest in applications such as quantum sorting. Quantum sorting plays a critical role in creating ordered sets of data that can be better utilized, e.g., quantum ordered search or quantum network switching. In this paper, we propose a quantum sorting algorithm that combines highly parallelizable bitonic merge networks with perfect shuffle permutations (PSP), for sorting data represented in the quantum domain. The combination of bitonic networks with PSP improves the temporal complexity of bitonic merge sorting which is critical for reducing decoherence effects for quantum processing. We present space-efficient quantum circuits that can be used for quantum bit comparison and permutation. We also present a reconfigurable hardware quantum emulator for prototyping the proposed quantum algorithm. The emulator has a fully-pipelined architecture and supports double-precision floating-point computations, resulting in high throughput and accuracy. The proposed hardware architectures are implemented on a high-performance reconfigurable computer (HPRC). In our experiments, we emulated quantum sorting circuits of up to 31 fully-entangled quantum bits on a single FPGA node of the HPRC platform. To the best of our knowledge, our effort is the first to investigate a reconfigurable hardware emulation of quantum sorting using bitonic networks and perfect shuffle.
Quantum computing is emerging as a transformative paradigm across various fields, from drug discovery to secure communications. This paper surveys current and potential applications of quantum computing, particularly focusing on chemistry, optimization, cryptography, finance, and remote sensing. We discuss the state of the art, existing hardware and software approaches, and future research challenges. The review highlights the role of quantum algorithms in outperforming classical methods in specific problem domains.
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.