Enhancing the reachability of variational quantum algorithms via input-state design
AI Breakdown
Get a structured breakdown of this paper — what it's about, the core idea, and key takeaways for the field.
Abstract
Variational quantum algorithms (VQAs) face an inherent trade-off between expressivity and trainability: deeper circuits can represent richer states but suffer from noise accumulation and barren plateaus, while shallow circuits remain trainable and implementable but lack expressive power. Here, we propose a general framework to address this challenge by enhancing the VQA performance with a specially designed input state constructed using a linear combination technique. This approach systematically modified the set of states reachable by the original circuit, enhancing accuracy while preserving efficiency. We provide a rigorous proof that such framework increases the expressive capacity of any given VQA ansatz, and demonstrate its broad applicability across different ansatz families. As applications, we apply the method to ground-state preparation of the transverse-field Ising, cluster-Ising, and Fermi-Hubbard models, achieving consistently higher accuracy under the same gate budget compared with standard VQAs. These results highlight input-state design as a powerful complement to circuit design in realizing VQAs that are both expressive and trainable.