用量子版LeNet压缩数据，让量子神经网络更快更省资源。
Dataset Distillation for Quantum Neural Networks
- 设计含残差连接和可训练厄米算符的量子LeNet模型。
- 在MNIST/Cifar-10上用少量数据实现91.9%/50.3%准确率。
- 适合想降低量子计算成本的研究者或初学者。
在大量经典数据上训练量子神经网络（QNN）既耗时又昂贵。数据越多，收敛所需梯度下降步数越多，导致量子执行次数增加，整体开销上升。本文提出针对QNN的数据蒸馏方法，采用包含残差连接和可训练厄米算符的新型量子版经典LeNet模型。该方法能在保持相近性能的前提下，生成少量高信息量的训练数据。我们在MNIST和Cifar-10数据集上进行蒸馏实验，结果表明，量子LeNet在推理后准确率分别为91.9%（MNIST）和50.3%（Cifar-10），与经典LeNet（94%、54%）相当。我们还引入不可训练厄米算符以提升蒸馏稳定性，观察到准确率略有下降（最多1.8%（MNIST）、1.3%（Cifar-10））。
原文摘要 · Abstract (English)
Training Quantum Neural Networks (QNNs) on large amount of classical data can be both time consuming as well as expensive. Higher amount of training data would require higher number of gradient descent steps to reach convergence. This, in turn would imply that the QNN will require higher number of quantum executions, thereby driving up its overall execution cost. In this work, we propose performing the dataset distillation process for QNNs, where we use a novel quantum variant of classical LeNet model containing residual connection and trainable Hermitian observable in the Parametric Quantum Circuit (PQC) of the QNN. This approach yields highly informative yet small number of training data at similar performance as the original data. We perform distillation for MNIST and Cifar-10 datasets, and on comparison with classical models observe that both the datasets yield reasonably similar post-inferencing accuracy on quantum LeNet (91.9% MNIST, 50.3% Cifar-10) compared to classical LeNet (94% MNIST, 54% Cifar-10). We also introduce a non-trainable Hermitian for ensuring stability in the distillation process and note marginal reduction of up to 1.8% (1.3%) for MNIST (Cifar-10) dataset.
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