Arthur Nádas
IEEE Transactions on Neural Networks
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.
Arthur Nádas
IEEE Transactions on Neural Networks
Michael Muller, Anna Kantosalo, et al.
CHI 2024
Gang Liu, Michael Sun, et al.
ICLR 2025
Annina Riedhauser, Viacheslav Snigirev, et al.
CLEO 2023