This paper presents an exploration of quantum algorithms using application-oriented performance benchmarks. The QED-C suite of Application-Oriented Benchmarks provides a framework to evaluate the performance of quantum computers in real-world applications. The work investigates challenges in broadening the relevance of this benchmarking methodology to more complex applications. Key contributions include the introduction of a scalable HHL linear equation solver benchmark, the addition of a VQE implementation for a Hydrogen Lattice simulation, and the exploration of a supervised machine learning classification application. The paper also discusses the inclusion of optimization and error mitigation techniques in the benchmarking workflow. The results show that accuracy decreases with more qubits, but execution time increases only mildly. The benchmark framework is shown to be useful for evaluating algorithmic options and their impact on performance. The paper also discusses the importance of benchmarking in facilitating the exploration of quantum algorithms and their performance characteristics. The QED-C benchmark framework is shown to be effective in evaluating the performance of quantum algorithms across different quantum computing systems. The paper concludes that the benchmark framework is a valuable tool for evaluating the performance of quantum algorithms and their impact on real-world applications.This paper presents an exploration of quantum algorithms using application-oriented performance benchmarks. The QED-C suite of Application-Oriented Benchmarks provides a framework to evaluate the performance of quantum computers in real-world applications. The work investigates challenges in broadening the relevance of this benchmarking methodology to more complex applications. Key contributions include the introduction of a scalable HHL linear equation solver benchmark, the addition of a VQE implementation for a Hydrogen Lattice simulation, and the exploration of a supervised machine learning classification application. The paper also discusses the inclusion of optimization and error mitigation techniques in the benchmarking workflow. The results show that accuracy decreases with more qubits, but execution time increases only mildly. The benchmark framework is shown to be useful for evaluating algorithmic options and their impact on performance. The paper also discusses the importance of benchmarking in facilitating the exploration of quantum algorithms and their performance characteristics. The QED-C benchmark framework is shown to be effective in evaluating the performance of quantum algorithms across different quantum computing systems. The paper concludes that the benchmark framework is a valuable tool for evaluating the performance of quantum algorithms and their impact on real-world applications.