Softer is Better: Tweaking Quantum Dropout to Enhance Quantum Neural Network Trainability

Quantum Machine Learning (QML) has been acknowledged for its transformative potential in enhancing computational capabilities beyond classical approaches, with Quantum Neural Networks (QNNs) emerging as one of the most promising models. Despite the advancements, the adaptation of effective optimization techniques such as batch normalization or regularization, which are well-established in classical Neural Networks, remains an ongoing challenge for QNNs.This adaptation is crucial, especially when considering the implementation of techniques like quantum dropout, which, although inspired by its classical counterpart to reduce the risk of overfitting by "deleting" certain model components during the forward step, exhibits distinct behaviours in a quantum context. This study introduces a novel approach to modulate the impact of dropout in QNNs through a technique we term Soft Dropout. Our method introduces a parameterized softening mechanism for gate eliminations, enabling a more nuanced control over the dropout process, thereby mitigating its adverse effects on the network’s learning capacity, and reducing at the same time the risk of overfitting.Our experimental analysis demonstrates that softening current quantum dropout consistently enhances model performance across a spectrum of configurations. This improvement is attributed to the intrinsic properties of quantum gates, which allow for a gradual adjustment of their impact on the quantum circuit up to the identity operation. The results highlight the importance of adapting classical machine learning techniques to the quantum context, considering the unique computational model of quantum processing, potentially accelerating the adoption of QML in real-world problems.