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.

arXiv comment30 pages, 5 figures, 6 tables

References in corpus (44)

• Quantum Computing in the NISQ era and beyond
• Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer
• Quantum Machine Learning
• Quantum Mechanics helps in searching for a needle in a haystack
• Variational Quantum Algorithms
• Supervised learning with quantum enhanced feature spaces
• Attention Mechanisms in Computer Vision: A Survey
• QuTiP: An open-source Python framework for the dynamics of open quantum systems
• Quantum support vector machine for big data classification
• Noisy intermediate-scale quantum (NISQ) algorithms
• Quantum machine learning in feature Hilbert spaces
• Quantum Circuit Learning
• Quantum Convolutional Neural Networks
• Quantum random access memory
• The effect of data encoding on the expressive power of variational quantum machine learning models
• Power of data in quantum machine learning
• Efficient Learning for Deep Quantum Neural Networks
• Quantum generative adversarial learning
• A rigorous and robust quantum speed-up in supervised machine learning
• Quantum Natural Gradient
• Quantum advantage with shallow circuits
• Continuous-variable quantum neural networks
• Strawberry Fields: A Software Platform for Photonic Quantum Computing
• Hierarchical quantum classifiers
• Hybrid Quantum-Classical Convolutional Neural Networks
• Hybrid Quantum-Classical Approach to Quantum Optimal Control
• Experimental Realization of Quantum Artificial Intelligence
• Q#: Enabling scalable quantum computing and development with a high-level domain-specific language
• Quantum generative adversarial learning in a superconducting quantum circuit
• Generalization in Quantum Machine Learning: a Quantum Information Perspective
• Machine learning meets quantum physics
• Estimating the gradient and higher-order derivatives on quantum hardware
• ScaffCC: Scalable Compilation and Analysis of Quantum Programs
• Quantum advantage with noisy shallow circuits in 3D
• Quantum Adversarial Machine Learning
• Statistical mechanics of the Cluster-Ising model
• Yao.jl: Extensible, Efficient Framework for Quantum Algorithm Design
• Fisher Information in Noisy Intermediate-Scale Quantum Applications
• Quantum Machine Learning for Particle Physics using a Variational Quantum Classifier
• Towards understanding the power of quantum kernels in the NISQ era
• Dimensional Expressivity Analysis of Parametric Quantum Circuits
• Expressivity of Quantum Neural Networks
• Quantum advantage for computations with limited space
• Representation of binary classification trees with binary features by quantum circuits

Cited by in corpus (8)

• Towards quantum enhanced adversarial robustness in machine learning
• Long-lived topological time-crystalline order on a quantum processor
• Universal adversarial perturbations for multiple classification tasks with quantum classifiers
• Learning Fourier series with parametrized quantum circuits
• Probing many-body Bell correlation depth with superconducting qubits
• Quantum Approximation of Normalized Schatten Norms and Applications to Learning
• Exact Quantum Algorithm for Unit Commitment Optimization based on Partially Connected Quantum Neural Networks
• Quantum-data-driven dynamical transition in quantum learning