BenchQC: A Benchmarking Toolkit for Quantum Computation
Abstract
The Variational Quantum Eigensolver (VQE) is a widely studied hybrid classical-quantum algorithm for approximating ground-state energies in molecular and materials systems. This study benchmarks the performance of the VQE for calculating ground-state energies of small aluminum clusters ( Al - $$ {\mathrm{Al}}^{-} $$ , Al 2 $$ {\mathrm{Al}}_2 $$ , and Al 3 - $$ {\mathrm{Al}}_3^{-} $$ ) within a quantum-density functional theory (DFT) embedding framework, systematically varying key parameters: (I) classical optimizers, (II) circuit types, (III) number of repetitions, (IV) simulator types, (V) basis sets, and (VI) noise models. All calculations were performed using quantum simulators to evaluate VQE performance under both idealized and noise-augmented conditions. Our findings demonstrate that certain optimizers converge efficiently, while circuit choice and basis set selection have a marked impact on energy estimates, with higher-level basis sets closely matching classical computation data from Numerical Python Solver (NumPy) and Computational Chemistry Comparison and Benchmark DataBase (CCCBDB). To approximate realistic conditions, we employed IBM noise models to simulate the effects of hardware noise. The results showed close agreement with CCCBDB benchmarks, with percent errors consistently below 0.2%. The results demonstrate that VQE can approximate energy estimates under simulated conditions for small aluminum clusters and highlight the importance of optimizing quantum-DFT…
Read the paper
Related studies
- Chaos and quantum thermalization
- The Dalton quantum chemistry program system
- Cord Blood Leptin and Adiponectin as Predictors of Adiposity in Children at 3 Years of Age: A Prospective Cohort Study
- Quantitative metabolomics by H-NMR and LC-MS/MS confirms altered metabolic pathways in diabetes
- Stable adiabatic quantum batteries
- A novel self-lipid antigen targets human T cells against CD1c(+) leukemias
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.