Evolutionary-enhanced quantum supervised learning model
This addresses a critical bottleneck in quantum machine learning for NISQ devices, offering a potential path to quantum advantage in supervised learning, though it appears incremental as it builds on existing quantum methods.
The study tackled the barren plateau problem in quantum supervised learning by proposing an evolutionary-enhanced ansatz-free model with variable topology circuits and superposition of multi-hot encodings, resulting in improved accuracy and training efficiency compared to state-of-the-art variational quantum classifiers.
Quantum supervised learning, utilizing variational circuits, stands out as a promising technology for NISQ devices due to its efficiency in hardware resource utilization during the creation of quantum feature maps and the implementation of hardware-efficient ansatz with trainable parameters. Despite these advantages, the training of quantum models encounters challenges, notably the barren plateau phenomenon, leading to stagnation in learning during optimization iterations. This study proposes an innovative approach: an evolutionary-enhanced ansatz-free supervised learning model. In contrast to parametrized circuits, our model employs circuits with variable topology that evolves through an elitist method, mitigating the barren plateau issue. Additionally, we introduce a novel concept, the superposition of multi-hot encodings, facilitating the treatment of multi-classification problems. Our framework successfully avoids barren plateaus, resulting in enhanced model accuracy. Comparative analysis with variational quantum classifiers from the technology's state-of-the-art reveal a substantial improvement in training efficiency and precision. Furthermore, we conduct tests on a challenging dataset class, traditionally problematic for conventional kernel machines, demonstrating a potential alternative path for achieving quantum advantage in supervised learning for NISQ era.