Optimizing Parameters for Analog Variational Quantum Eigensolvers

Quantum computing is a powerful new tool still in its early stages, but it can be used along with classical computing methods to efficiently solve certain types of problems. One promising hybrid algorithm is the Variational Quantum Eigensolver (VQE), which estimates the expectation values or eigenvalues of a model Hamiltonian. VQEs operate via the Variational Principle, where a "guess" quantum state is iteratively optimized until the system energies are minimized. While most VQEs rely on digital quantum circuits, an analog approach via direct optimization of hardware controls is likely more advantageous for fast VQE state preparation on noisy intermediate-scale quantum devices. This work explores how the choice of physical quantum architecture affects VQE performance, with the goal of reducing computational time and errors.