进化增强的量子监督学习模型
Evolutionary-enhanced quantum supervised learning model
AI总结：
提出进化增强的无拟设量子监督模型，通过可变拓扑电路和精英进化避免贫瘠高原，引入多热编码叠加处理多分类，提升训练效率与精度。
摘要：
利用变分电路的量子监督学习，因其在创建量子特征映射和实现具有可训练参数的硬件高效拟设时对硬件资源利用的高效性，成为NISQ设备的一项有前景的技术。尽管有这些优势，量子模型的训练仍面临挑战，特别是贫瘠高原现象，导致优化迭代过程中学习停滞。本研究提出了一种创新方法：一种进化增强的无拟设监督学习模型。与参数化电路不同，我们的模型采用具有可变拓扑的电路，通过精英方法进化，缓解了贫瘠高原问题。此外，我们引入了一个新概念，即多热编码的叠加，便于处理多分类问题。我们的框架成功避免了贫瘠高原，从而提高了模型准确性。与最先进技术中的变分量子分类器的比较分析显示，训练效率和精度有显著提升。此外，我们在一个传统上对常规核机器具有挑战性的数据集类别上进行了测试，展示了在NISQ时代实现量子监督学习优势的潜在替代路径。
英文摘要：
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.
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