Programming Quantum Computersfor Aerospace Research

Tobias Stollenwerk

Fred C. Briggs

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.

Tobias Stollenwerk

Tobias Stollenwerk

We report on the mapping of hard optimization problems from aerospace research to the D-Wave quantum annealer.

Mohammed Az Syed, Paul Garcia

Aerospace vehicle detection, localization, and subsequent parametric computations such as range and altitude in frame-based data analysis acquired through vision sensors such as cameras can be a challenging problem due to a variety of factors including but not limited to the size and shape of the vehicle, the distance and position of the vehicle, lighting conditions, and frame resolution. A combination of machine learning and computer vision techniques are generally used to address these situations. In this paper, we demonstrate a workflow using quantum-enhanced, transfer and classical machine learning to detect and localize aerospace vehicles and compute parameters such as range and altitude from color histograms. Additionally, we describe a data synthesis approach for training machine learning algorithms against aerospace imagery. The purpose of our study is not to present the superiority of a technique but rather the feasibility and practicality of incorporating quantum enhancement in aerospace data analytics, data synthesis through simulation programs, and parametric computations through color histograms.

Osama M. Raisuddin, Suvranu De