A Noisy Optimization mechanism for variational quantum classifiers
Rodica Ioana Lung, Florin Sebastian Duma · Knowledge-Based Systems · 2026
• existence of barren plateaux is one of the challenges in the practical use of variational quantum classifiers; • a noise-based mechanism that shifts training data during optimization, helping escape barren plateaux, is proposed; • the approach is tested with a variational quantum classifier modeling BET index changes using other indices from Europe and the United States; • simulations use the Pennylane framework. The barren plateaux phenomenon has been identified as a significant challenge for variational quantum algorithms, particularly for classification tasks. In this article, we propose a novel approach to mitigating this problem for variational quantum classifiers during the optimization phase. The noisy optimization mechanism shifts the training data by adding a small amount of uniform noise, thereby inducing changes in the parameters being searched. The effectiveness of the method is evaluated using real financial data, modeling the evolution of the BET index in relation to well-known indices from neighboring Central and Eastern European countries, as well as from Western Europe and the United States. The results demonstrate that this approach significantly improves upon the corresponding baseline quantum classifier and provides results comparable to those of established classical methods.