Characterizing Hybrid Quantum Algorithms for Quantum Performance Benchmarks

Hybrid quantum algorithms have emerged as potential candidates to gain computational advantage on Noisy Intermediatory-Scale Quantum (NISQ) Computers. Here we discuss two such algorithms used in Quantum Chemistry and Machine Learning: Variational Quantum Eigensolver (VQE) and Quantum Convolution Neural Networks (QCNN). We evaluate the performance of these algorithms in the context of an advanced benchmarking framework for quantum computers designed to estimate resource utilization and calculate application-specific metrics. This allows us to examine the trade-off between run-time execution performance and the quality of solutions for iterative hybrid quantum-classical applications and also highlight the limitations of the algorithms. The benchmarking suite can be executed on simulators as well as existing hardware and is an enhancement to the QED-C's Application-Oriented Benchmark suite.