VSQL: Variational Shadow Quantum Learning for Classification

TL;DR

VSQL introduces a hybrid quantum-classical framework utilizing classical shadows and variational circuits for efficient, low-parameter quantum classification, avoiding barren plateaus and improving accuracy in quantum and classical data tasks.

Contribution

The paper presents VSQL, a novel hybrid approach that leverages classical shadows and variational circuits to enhance quantum classification efficiency and accuracy.

Findings

VSQL reduces the number of parameters needed for quantum classifiers.

VSQL avoids the barren plateau problem in quantum training.

VSQL outperforms existing variational quantum classifiers in accuracy.

Abstract

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. We show that this method could sharply reduce the number of parameters and thus better facilitate quantum circuit training. Simultaneously, less noise will be introduced since fewer quantum gates are employed in such shadow circuits. Moreover, we show that the Barren Plateau issue, a significant gradient vanishing problem in quantum machine learning, could be avoided in VSQL. Finally, we demonstrate the efficiency of VSQL in quantum classification via numerical experiments on the classification of quantum states and the recognition of multi-labeled handwritten digits. In particular, our VSQL approach outperforms existing variational quantum classifiers in the test accuracy in the binary case of handwritten digit recognition and notably requires much fewer parameters.