BenchQC: A Benchmarking Toolkit for Quantum Computation.

Area of Science:

• Quantum Computing
• Computational Chemistry
• Materials Science

Background:

• The Variational Quantum Eigensolver (VQE) is a key hybrid algorithm for quantum chemistry.
• Accurate ground-state energy calculations are vital for molecular and materials science.
• Benchmarking VQE performance is essential for its practical application.

Purpose of the Study:

• To benchmark the performance of VQE for calculating ground-state energies of small aluminum clusters.
• To systematically evaluate the impact of various parameters on VQE accuracy.
• To assess VQE performance under simulated noise conditions.

Main Methods:

• Utilized a quantum-density functional theory (DFT) embedding framework.
• Performed calculations using quantum simulators with varied classical optimizers, circuit types, basis sets, and noise models.
• Employed IBM noise models to simulate hardware noise effects.

Main Results:

• Certain classical optimizers demonstrated efficient convergence.
• Circuit type and basis set selection significantly influenced energy estimates.
• VQE results under simulated noise closely matched established benchmarks (CCCBDB) with <0.2% error.

Conclusions:

• VQE can provide accurate energy estimates for small aluminum clusters even under simulated noise.
• Optimizing quantum-DFT parameters is critical for balancing computational cost and precision.
• This study provides valuable insights for developing future VQE benchmarking tools in quantum chemistry.