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

Abstract

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.

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Cited by in corpus (4)

• Two-Step Quantum Search Algorithm for Solving Traveling Salesman Problems
• Inductive Construction of Variational Quantum Circuit for Constrained Combinatorial Optimization
• Consensus-based qubit configuration optimization for variational algorithms on neutral atom quantum systems
• Quick design of feasible tensor networks for constrained combinatorial optimization