Abstract Variational quantum algorithms (VQAs) are constrained by a trade-off: deeper circuits can cover a larger reachable quantum states but suffer from barren plateaus, while shallow circuits remain trainable yet can have insufficient reachability to the target state. Here, we propose a general framework to address this challenge by enhancing the VQA performance with a designed input state constructed using a linear combination technique. This approach modifies the set of states reachable by the original circuit, enhancing accuracy while preserving efficiency. We provide a rigorous proof that such framework increases the performance of any given VQA ansatz, and demonstrate its broad applicability across different ansatz families. In ground state preparation for representative quantum many-body models, it achieves consistently higher accuracy than standard methods at the same gate budget. These results highlight input-state design as a powerful complement to circuit design for improving reachability of the target state within a fixed ansatz. Data availability The data supporting the findings of this study are available from the corresponding author, Xiaoting Wang, upon request. Code availability The code used in this study is available from the corresponding author, Xiaoting Wang, upon request. References Preskill, J. Quantum computing in the NISQ era and beyond. Quantum 2, 79 (2018). Bharti, K. et al. Noisy intermediate-scale quantum algorithms. Rev. Mod. Phys. 94, 015004 (2022). Tilly, J. et al. The variational quantum eigensolver: a review of methods and best practices. Phys. Rep. 986, 1–128 (2022). Fedorov, D. A., Peng, B., Govind, N. & Alexeev, Y. Vqe method: a short survey and recent developments. Mater. Theory 6, 2 (2022). Cerezo, M. et al. Variational quantum algorithms. Nat. Rev. Phys. 3, 625–644 (2021). Cerezo, M., Verdon, G., Huang, H.-Y., Cincio, L. & Coles, P. J. Challenges and opportunities in quantum machine learning. Nat. Comput. Sci. 2, 567–576 (2022). Bharti,