Benchmarking VQE Configurations: Architectures, Initializations, and Optimizers for Silicon Ground State Energy
This provides a structured benchmark for selecting optimal VQE settings in quantum chemical simulations, though it is incremental as it applies existing methods to a specific heavy element.
This work systematically benchmarks different VQE configurations for estimating the silicon atom's ground-state energy, finding that parameter initialization is crucial for stability and that chemically-inspired ansatzes with adaptive optimization yield superior convergence and precision.
Quantum computing presents a promising path toward precise quantum chemical simulations, particularly for systems that challenge classical methods. This work investigates the performance of the Variational Quantum Eigensolver (VQE) in estimating the ground-state energy of the silicon atom, a relatively heavy element that poses significant computational complexity. Within a hybrid quantum-classical optimization framework, we implement VQE using a range of ansatz, including Double Excitation Gates, ParticleConservingU2, UCCSD, and k-UpCCGSD, combined with various optimizers such as gradient descent, SPSA, and ADAM. The main contribution of this work lies in a systematic methodological exploration of how these configuration choices interact to influence VQE performance, establishing a structured benchmark for selecting optimal settings in quantum chemical simulations. Key findings show that parameter initialization plays a decisive role in the algorithm's stability, and that the combination of a chemically inspired ansatz with adaptive optimization yields superior convergence and precision compared to conventional approaches.