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.

本研究提出了一种创新的方法，利用进化增强的无参数化电路进行监督学习模型，该模型采用可变拓扑的电路经由精英方法演化，从而减轻了贫瘠高原问题，并引入了多热编码的叠加概念，方便多分类问题的处理。通过与当前技术最先进的变分量子分类器进行比较分析，我们的框架在训练效率和精确度方面得到了显著的提升。此外，我们对传统上常常困扰传统核心机器的具有挑战性的数据集类进行了测试，展示了在 NISQ 时代实现量子优势的潜在替代路径。