Work

Efficiently Architecting VQAs: Expressibility–Trainability–Resources Pareto-Optimality

Year: 2026

Type: article

Abstract: Ansatz selection is a key factor in the performance of variational quantum algorithms (VQAs). While much of the state-of-the-art still relies on heuristic choices, an inadequate circuit structure can compromise both the expressive power and the trainability of the resulting model. Recent results have also established theoretical connections between expressibility and the onset of barren plateaus, highlighting the need for systematic criteria for ansatz selection. In this work, the ansatz is treated as a design feature to be optimized rather than a fixed block, and a design space exploration (DSE) is performed over a diverse set of parametrized quantum circuits (PQCs). Three complementary metrics -- expressibility, trainability, and resource cost -- are evaluated and used to analyze the trade-offs that emerge across different PQCs. Beyond identifying Pareto-optimal candidates, this multi-objective perspective helps clarify the interplay between these metrics and contributes quantitative evidence toward understanding the expressibility--trainability tension in variational circuits.

Source: IEEE Transactions on Quantum Engineering

DOI: 10.1109/tqe.2026.3721256

Authors: Rodrigo Martinez Sanz, Andreu Angles-Castillo, Eduard Alarcon, Carmen G. Almudever

Institutions: Universitat Politècnica de València, Universitat Politècnica de Catalunya

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,075