Minisymposium Presentation

The variational quantum algorithms have a special importance in the research on quantum computing applications, for their applicability on current noisy intermediate-scale quantum (NISQ) devices. The main building blocks of these algorithms (e.g., the definition of the Hamiltonian and of the ansatz, the parameter optimization) define a relatively large parameter space, making the comparison of results and performance between different software simulators cumbersome and prone to errors. In this work, we employ a generic description of the problem, in terms of both Hamiltonian and ansatz to port a problem definition consistently among different simulators. Three use cases of relevance for current quantum hardware (ground state calculation for H_2 molecule, MaxCut, Travelling Salesman Problem) have been run on a set of HPC systems (shared-memory, multi-node, GPU-accelerated). The results show that, for some of the most used QC simulation packages, simple variational workloads present performance and scaling challenges. The presented parser allows a fair comparison of the performance while ensuring that problem definition and physical results stay consistent.