  The Variational Quantum Eigensolver (VQE) algorithm is gaining interest for
its 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 given
Hamiltonian. Designing efficient PQCs is crucial for improving convergence
speed. In this study, we introduce problem-specific PQCs tailored for
optimization problems by dynamically generating PQCs that incorporate problem
constraints. This approach reduces a search space by focusing on unitary
transformations that benefit the VQE algorithm, and accelerate convergence. Our
experimental results demonstrate that the convergence speed of our proposed
PQCs outperforms state-of-the-art PQCs, highlighting the potential of
problem-specific PQCs in optimization problems.
