Quantum computing is rapidly becoming an invaluable component of aerospace analysis. Selecting a programing language that enables efficient quantum algorithm development and integrates seamlessly with classical simulation tools is essential for translating these capabilities into practice. This paper is a general literature survey of recent research and publications that will attempt to answer the question: What may be appropriate quantum computing software programming language(s) for aerospace applications? What quantum programming language(s) would be suitable for scientific and engineering calculations as FORTRAN was for classical computing? Quantum computing has the potential to make complex aerospace simulations like Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), material, and boundary layer analysis much faster and more efficient. Successful quantum enhancement work has already been completed towards secure quantum space networks, space weather detection and early warning alerts, satellite image processing, and flight path optimization. There is a plethora (thirty-six plus) of quantum computing languages and toolkits today. How do they deal effectively with the unique characteristics of quantum computing like entanglement and the no cloning theory? The differences between Artificial Intelligence (AI) and quantum computing will be discussed and a survey of the current quantum computing software and tools will be presented. A framework for choosing an appropriate quantum programming language for aerospace applications is also provided. Findings from the literature show Python used with the Qiskit platform could be the most useful for aerospace applications.
Quantum computing is rapidly becoming an invaluable component of aerospace analysis. Selecting a programing language that enables efficient quantum algorithm development and integrates seamlessly with classical simulation tools is essential for translating these capabilities into practice. This paper is a general literature survey of recent research and publications that will attempt to answer the question: What may be appropriate quantum computing software programming language(s) for aerospace applications? What quantum programming language(s) would be suitable for scientific and engineering calculations as FORTRAN was for classical computing? Quantum computing has the potential to make complex aerospace simulations like Finite Element Analysis (FEA), Computational Fluid Dynamics (CFD), material, and boundary layer analysis much faster and more efficient. Successful quantum enhancement work has already been completed towards secure quantum space networks, space weather detection and early warning alerts, satellite image processing, and flight path optimization. There is a plethora (thirty-six plus) of quantum computing languages and toolkits today. How do they deal effectively with the unique characteristics of quantum computing like entanglement and the no cloning theory? The differences between Artificial Intelligence (AI) and quantum computing will be discussed and a survey of the current quantum computing software and tools will be presented. A framework for choosing an appropriate quantum programming language for aerospace applications is also provided. Findings from the literature show Python used with the Qiskit platform could be the most useful for aerospace applications.
This paper is a general literature survey of recent research and publications that will attempt to answer the question: “What are the uses of quantum computing in aerospace systems.” Quantum computing is still in the very early stages of development and appears to be at a technology readiness level where subcomponents are being tested in a relevant environment. For example, NASA’s Jet Propulsion Laboratory (JPL) is planning to send an experiment to the International Space Station that could lead the way to a quantum global network. It will test communication technologies in the harsh environment of space that could enable two quantum computers to securely connect with each other no matter where they were. This paper will reference JPL’s experiment1.and attempt to summarize quantum computing aerospace applications. The use of machine learning algorithms appears to be one of the most significant enablers of quantum computing. Quantum cryptography and cryptanalysis seem to be the most important applications, among many others since our national security and future lunar exploration competitiveness may depend on it.
Quantum computing is no longer only a scientific interest but is rapidly becoming an industrially available technology that can potentially overcome the limits of classical computation. Over the last years, all major companies have provided frameworks and programming languages that allow developers to create their quantum applications. This shift has led to the definition of a new discipline called quantum software engineering, which is demanded to define novel methods for engineering large-scale quantum applications. While the research community is successfully embracing this call, we notice a lack of systematic investigations into the state of the practice of quantum programming. Understanding the challenges that quantum developers face is vital to precisely define the aims of quantum software engineering. Hence, in this paper, we first mine all the GitHub repositories that make use of the most used quantum programming frameworks currently on the market and then conduct coding analysis sessions to produce a taxonomy of the purposes for which quantum technologies are used. In the second place, we conduct a survey study that involves the contributors of the considered repositories, which aims to elicit the developers' opinions on the current adoption and challenges of quantum programming. On the one hand, the results highlight that the current adoption of quantum programming is still limited. On the other hand, there are many challenges that the software engineering community should carefully consider: these do not strictly pertain to technical concerns but also socio-technical matters.
This paper discusses the benefits that can be enjoyed from the possible application of quantum computing to the aerospace sector. It discusses these benefits for various tasks and applications such as artificial intelligence, cryptographic key distribution and simulation. The challenge of determining what aerospace application computations will be best allocated to quantum devices is also discussed. The method for best utilizing quantum computing for aerospace applications is considered and the benefits of a grid-based implementation of computational resources are discussed. The grid model of having a collection of network-connected heterogenous devices could be used as a way to make effective use of computational resources. It could be used for the operations and development of simulations as well as machine learning and other software applications. This paper also presents a summary of potential use-cases, as well as a discussion of the work necessary to expedite the process of quantum computing development.
Over the last few years, quantum computing has been growing at an exponential pace. Every day, new techniques, frameworks, modeling, and programming languages are emerging that aim to facilitate the development of quantum software, which is key to achieving the promising applications of quantum computing. However, which of these are actively used and the degree of satisfaction of researchers and developers regarding these quantum software frameworks and languages is not known. To address this, we conducted a survey to characterize which modeling tools and which quantum programming languages are used during the quantum software lifecycle. Researchers in academia and industry developers were surveyed, and a total of 57 responses were collected. The results indicate that during quantum software development, some models and diagrams are used to guide development. In addition, the survey results show what quantum programming languages are the most used alongside the classical programming languages employed to build hybrid programs, among other important insights. The implications of this survey are: (i) to find out what the current trends are within quantum software development and (ii) to find out what the needs are of quantum software developers with respect to current modeling and programming languages and tools.
In the contemporary era of scientific and technical innovations, we are witnessing remarkable progress in the realm of quantum computing. Today’s phase is referred to as the second quantum revolution, characterized by ongoing research and progress in the hardware, software, and applications of quantum computers. While the theoretical foundations of quantum computing have been in place for decades, the practical tools and technologies that have emerged in recent years have catapulted this field from theory into reality. This paper provides a brief overview of the fundamental principles of quantum computing and explores the various technologies that support them. From quantum programming languages and simulators to quantum hardware platforms and software development kits, these tools have paved the way for groundbreaking research, experimentation, and the exploration of quantum’s boundless potential. Furthermore, it addresses the current developments, existing challenges, ongoing improvements, and future prospects in this dynamic field.
Introduction: There is a great potential that the quantum computing can change the way of algorithms and software development more than classical computers. Thus, this article will try to focus on how algorithm design and software development can be affected by quantum computing as well as what possibilities could appear when quantum principles are implemented into traditional paradigms. This paper aims at identifying the impact of quantum computing on algorithm and software advancement, through a discussion of essential quantum algorithms, quantum languages, as well as the opportunities and challenges of quantum technologies. Method: An extensive literature review and theoretical investigation was also performed to investigate the foundational concepts of quantum computing and subsequent effects on algorithm and software engineering. Some of the research questions included exploring the contrast between classical and quantum algorithms, reviewing current literature on quantum programming languages, and delving into examples of real-life deployments of quantum algorithms cross numerous domains. Results: This paper shows that quantum computing brings qualitatively new paradigms in the algorithm design and function while the quantum algorithms such as Shor’s and Grover’s perform exponentially faster certain problems. Software development for quantum has brought the need to devise new frameworks of coding in light of probability in quantum circuit. It is also comforting to note that there is still effort being made although in its most embryonic form to create quantum programming languages like Qiskit and Cirq. Some of challenges include quantum decoherence; limited number of quantum hardware; and need for strong error correction processes.Conclusion: While there are currently relatively few quantum algorithms it is believed that the findings in this field have the ability to revolutionize algorithm and software design and subjects like cryptography, optimization and AI. However, trends in quantum computing show that the constraints to computational capabilities are likely to be lifted to allow creativity to develop the most powerful software solutions