论文标题
确定性张量网络分类器
Deterministic Tensor Network Classifiers
论文作者
论文摘要
我们提出张量网络，以提取特征和分类器性能的完善。这些网络可以确定性初始化，并具有实施近期中级量子（NISQ）设备的潜力。功能提取通过直接组合和压缩图像对幅度编码的图像进行编码，仅在$ \ log n _ {\ text {pixels}} $ qubits上进行。性能是使用“量子堆叠”来完善的，这是一种确定性方法，可以应用于任何分类器的预测，无论结构如何，并使用数据重新上传在NISQ设备上实现。这些过程应用于数据的张量网络编码，并针对10类MNIST和时尚MNIST数据集进行了基准测试。在没有任何各种培训的情况下，可以实现良好的训练和测试精度。
We present tensor networks for feature extraction and refinement of classifier performance. These networks can be initialised deterministically and have the potential for implementation on near-term intermediate-scale quantum (NISQ) devices. Feature extraction proceeds through a direct combination and compression of images amplitude-encoded over just $\log N_{\text{pixels}}$ qubits. Performance is refined using `Quantum Stacking', a deterministic method that can be applied to the predictions of any classifier regardless of structure, and implemented on NISQ devices using data re-uploading. These procedures are applied to a tensor network encoding of data, and benchmarked against the 10 class MNIST and fashion MNIST datasets. Good training and test accuracy are achieved without any variational training.
