NASA Nearest Earth Object Classification Using Quantum Machine Learning: A Survey

Quantum computing has emerged as a revolutionary field with the potential to revolutionize computation and solve complex problems that are beyond the capabilities of classical computers. This literature review paper presents a comprehensive examination of the tools, libraries, and algorithms developed in the realm of quantum computing. The review aims to provide an extensive analysis of the current state of the field and explore the advancements made in harnessing quantum phenomena for computational purposes. The literature review begins by delving into the fundamental principles of quantum computing, including the concept of qubits, superposition, entanglement, and quantum parallelism. It then navigates through the developments made by companies like D-Wave, IBM and Google in the field of quantum computing and the libraries developed such as Qbsolv, Qiskit, and Cirq, exploring their features, functionalities, and applications. Additionally, an in-depth exploration of prominent quantum algorithms, including Grover's algorithm, Shor's algorithm, Quantum Bayesian networks, and Quantum Support Vector Machines (SVM), is presented alongside their classical counterparts for comparative analysis. By critically evaluating the existing literature, this review aims to identify the strengths, limitations, and potential applications of these tools and algorithms. Additionally, it sheds light on the significance of utilizing quantum computing in celestial object identification, showcasing the comparative analysis between classical and quantum algorithms for improved risk assessment in space exploration.