  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.
