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The Variational Quantum Eigensolver (VQE) algorithm is gaining interest forits potential use in near-term quantum devices. In the VQE algorithm,parameterized quantum circuits (PQCs) are employed to prepare quantum states,which are then utilized to compute the expectation value of a givenHamiltonian. Designing efficient PQCs is crucial for improving convergencespeed. In this study, we introduce problem-specific PQCs tailored foroptimization problems by dynamically generating PQCs that incorporate problemconstraints. This approach reduces a search space by focusing on unitarytransformations that benefit the VQE algorithm, and accelerate convergence. Ourexperimental results demonstrate that the convergence speed of our proposedPQCs outperforms state-of-the-art PQCs, highlighting the potential ofproblem-specific PQCs in optimization problems.

Enhancing VQE Convergence for Optimization Problems with Problem-specific Parameterized Quantum Circuits