Quantum computing (QC) is a nascent computing paradigm that holds tremendous promise for solving problems that are computationally intractable for classical computers. As QC technology advances, it is essential to create new benchmarking suites with which QC performance can be evalutated. The primary objective of this thesis is to perform application-specific benchmarking analysis to compare different quantum hardware platforms. The study investigates three distinct hardware architectures for quantum computing: ion traps, superconductors, and quantum annealers. I seek to empirically measure the performance of various quantum hardware platforms to determine their optimal usage scenarios. To be more precise, this thesis involves the testing of three different use cases to evaluate quantum hardware performance: the simulation of an hydrogen molecule, a quantum-classical convolutional neural network (QCCNN), and solving a traveling salesman problem. Finally, this work also analyzes which optimizer and ansatz are best suited for the current hardware.