VSQL: Variational Shadow Quantum Learning for Classification

Understand

Classification of quantum data is essential for quantum machine learning and near-term quantum technologies.

• In this paper, we propose a new hybrid quantum-classical framework for supervised quantum learning, which we call Variational Shadow Quantum Learning (VSQL).
• Our method in particular utilizes the classical shadows of quantum data, which fundamentally represent the side information of quantum data with respect to certain physical observables.
• Specifically, we first use variational shadow quantum circuits to extract classical features in a convolution way and then utilize a fully-connected neural network to complete the classification task.