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.

翻译：量子监督学习利用变分电路，因其在创建量子特征图谱及实现具有可训练参数的硬件高效拟设时对硬件资源的高效利用，成为NISQ设备中一项具有前景的技术。尽管具备这些优势，量子模型的训练仍面临挑战，尤其是贫瘠高原现象，导致优化迭代过程中学习停滞。本研究提出一种创新方法：进化增强的无拟设监督学习模型。与参数化电路不同，我们的模型采用拓扑结构可变的电路，通过精英方法演化，从而缓解贫瘠高原问题。此外，我们引入新概念——多热编码的叠加态，以处理多分类问题。该框架成功规避了贫瘠高原，提升了模型精度。与当前技术水平的变分量子分类器对比分析显示，训练效率与精度均显著提升。进一步，我们在传统核机器难以处理的挑战性数据集类别上进行测试，证明了在NISQ时代监督学习中实现量子优势的潜在替代路径。