In the past few years, quantum computers have become increasingly robust and are reaching industrial strength in terms being considered seriously for addressing real life problems. The potential of quantum computers to impact almost every industry vertical and scientific research is well documented. It is only now that this potential can be realized to a certain extent. There are several areas that quantum computing spawns including quantum machine learning, quantum simulation, quantum metrology, quantum cryptography and secure quantum communications. One of the most important applications of secure quantum communications is the quantum network. A quantum network is a complex ecosystem in quantum computing which uses numerous quantum technologies to be implemented including quantum cryptography, quantum entanglement and quantum teleportation. Given the increasing maturity in terms of the hardware required to develop the quantum network, there is a need to focus on identifying and proposing use cases for quantum networks across various industry verticals and scientific domains which may not be implementable using the current classical computing paradigm. This paper evaluates and lists the use cases and applications using the quantum networks. We focus on some of the conventional industry verticals and newer industries and scientific areas where the use cases can be proposed.
In the past few years, quantum computers have become increasingly robust and are reaching industrial strength in terms being considered seriously for addressing real life problems. The potential of quantum computers to impact almost every industry vertical and scientific research is well documented. It is only now that this potential can be realized to a certain extent. There are several areas that quantum computing spawns including quantum machine learning, quantum simulation, quantum metrology, quantum cryptography and secure quantum communications. One of the most important applications of secure quantum communications is the quantum network. A quantum network is a complex ecosystem in quantum computing which uses numerous quantum technologies to be implemented including quantum cryptography, quantum entanglement and quantum teleportation. Given the increasing maturity in terms of the hardware required to develop the quantum network, there is a need to focus on identifying and proposing use cases for quantum networks across various industry verticals and scientific domains which may not be implementable using the current classical computing paradigm. This paper evaluates and lists the use cases and applications using the quantum networks. We focus on some of the conventional industry verticals and newer industries and scientific areas where the use cases can be proposed.
Despite the significant scientific and engineering hurdles in advancing quantum computers, notable strides are being made toward utilizing this technology in commercial domains. Quantum computers are projected to exceed the computational power of classical computers within the next decade, potentially revolutionizing various industries. This study explores a range of fields that have already begun integrating quantum hardware. By presenting these as examples of combinatorial problems, we demonstrate their applications across three key sectors: cryptography, material science, and Machine Learning. Further, we will conduct a survey on various industries and companies to identify the broader usage of quantum computing in their manufacturing. The survey will analyze the diverse potential applications and current advantages of quantum hardware and algorithms. The results will help further researchers identify and segment the companies and understand the rapid application of quantum computing.
Quantum computing represents a transformative paradigm in modern computation, leveraging the principles of quantum mechanics to solve complex problems beyond the reach of classical computers. With applications across diverse fields, quantum computing plays a pivotal role in advancing cryptography, artificial intelligence, drug discovery and etc..Additionally, quantum computing enhances artificial intelligence by optimizing machine learning models, improves financial modeling, and supports climate simulation by processing vast datasets and intricate algorithms at unprecedented speeds. Its potential also extends to revolutionizing secure communications, personalized medicine, and sustainable energy solutions. As researchers and industries increasingly explore this field, quantum computing is poised to address previously intractable problems and redefine technological innovation. This chapter highlights its diverse and impactful applications, underscoring its promise to reshape industries and create groundbreaking advancements.
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.
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.
Computational technologies drive progress in industry, science, government, and society. While these technologies form the foundation for intelligent systems and enable scientific and business innovation, they are also the limiting factors for progress. Quantum computing promises to overcome these limitations with better and faster solutions for optimization, simulation, and machine learning problems. Quantum computing is broadly applicable to business problems in optimization, machine learning, and simulation, impacting all industries. In the last couple of years, researchers investigated if quantum computing can help to improve classical machine learning algorithms. Therefore, it is instrumental for industry to seek an active role in this emergent ecosystem. In this chapter, the authors present a brief overview on various applications of quantum computing in machine learning.
Quantum computing serves as a revolutionary paradigm in computing, harnessing quantum mechanics principles such as quantum entanglement and superposition for optimization simulation, machine learning, and other problems that are a challenge to classical computer systems. The article will examine different usage opportunities in retail and supply chain operations, healthcare and life sciences domains, insurance operations, and financial service industries. Better computation can be achieved with quantum algorithms in discrete and continuous optimization, logistics network optimization, warehouse management, and product delivery optimized by quantum algorithms. Application domains in healthcare include simulation of molecules for medical breakthroughs, genomic analysis, and medical studies optimized by quantum eigenvalue algorithms and quantum eigensolver algorithms. The insurance industry portfolio targets catastrophic modeling, identification, and risk assessment optimized by quantum amplitude estimation algorithms and quantum machine learning algorithms. Financial service industries include derivative pricing, portfolio optimization, credit risk evaluation, and real-time risk calculation optimized in quantum algorithms. The path towards achieving quantum benefit in quantum technology involves using noisy intermediate quantum technology with hybrid quantum and classical designs and error management schemes, where integration opportunities for different industries arise from their common computation problem schemas in need of further scaling in hardware technology.