Quantum Neural Network Classifiers: A Tutorial

Abstract

Machine learning has achieved dramatic success over the past decade, with applications ranging from face recognition to natural language processing. Meanwhile, rapid progress has been made in the field of quantum computation including developing both powerful quantum algorithms and advanced quantum devices. The interplay between machine learning and quantum physics holds the intriguing potential for bringing practical applications to the modern society. Here, we focus on quantum neural networks in the form of parameterized quantum circuits. We will mainly discuss different structures and encoding strategies of quantum neural networks for supervised learning tasks, and benchmark their performance utilizing Yao.jl, a quantum simulation package written in Julia Language. The codes are efficient, aiming to provide convenience for beginners in scientific works such as developing powerful variational quantum learning models and assisting the corresponding experimental demonstrations.

Universal adversarial perturbations for multiple classification tasks with quantum classifiers

“…The quantum classifiers are specially gifted to process quantum data. According to the evidence provided in [12,21], quantum data can be efficiently learned by quantum classifiers and attacked by adversarial machine learning algorithms.…”

“…In our experiment, we choose a 12-qubit symmetry-protected topological (SPT) state dataset similar to [12] to verify the effectiveness of universal perturbation. Specifically, in the dataset containing SPT states, we examine a one-dimensional cluster-Ising model featuring periodic boundary conditions.…”

“…The attempting quantum computing characteristics hold the intriguing potential to trigger a revolution in traditional machine learning study [3][4][5][6][7][8][9][10]. Along this direction, a series of careful investigations have been conducted and various kinds of quantum classifiers have been introduced in [11][12][13][14]. These results imply potential unparalleled advantages for quantum classifiers compared with their classical counterparts [12].…”

“…Along this direction, a series of careful investigations have been conducted and various kinds of quantum classifiers have been introduced in [11][12][13][14]. These results imply potential unparalleled advantages for quantum classifiers compared with their classical counterparts [12]. The proposed quantum classifiers are typically composed of 'parameterized layers' [12] and 'entangled layers' [12].…”

“…These results imply potential unparalleled advantages for quantum classifiers compared with their classical counterparts [12]. The proposed quantum classifiers are typically composed of 'parameterized layers' [12] and 'entangled layers' [12]. Such classifiers can reach the state-of-art accuracy of over 95% on binary classification tasks by utilizing limited computational resources in acceptable time complexity.…”