Classification with Quantum Machine Learning: A Survey

Abstract

Due to the superiority and noteworthy progress of Quantum Computing (QC) in a lot of applications such as cryptography, chemistry, Big data, machine learning, optimization, Internet of Things (IoT), Blockchain, communication, and many more. Fully towards to combine classical machine learning (ML) with Quantum Information Processing (QIP) to build a new field in the quantum world is called Quantum Machine Learning (QML) to solve and improve problems that displayed in classical machine learning (e.g. time and energy consumption, kernel estimation). The aim of this paper presents and summarizes a comprehensive survey of the state-of-the-art advances in Quantum Machine Learning (QML). Especially, recent QML classification works. Also, we cover about 30 publications that are published lately in Quantum Machine Learning (QML). we propose a classification scheme in the quantum world and discuss encoding methods for mapping classical data to quantum data. Then, we provide quantum subroutines and some methods of Quantum Computing (QC) in improving performance and speed up of classical Machine Learning (ML). And also some of QML applications in various fields, challenges, and future vision will be presented.

References in corpus (29)

• Quantum Computing in the NISQ era and beyond
• Quantum Machine Learning
• Quantum algorithm for solving linear systems of equations
• The theory of variational hybrid quantum-classical algorithms
• Quantum support vector machine for big data classification
• Quantum machine learning in feature Hilbert spaces
• Quantum Circuit Learning
• Quantum Algorithms Revisited
• Quantum algorithms: an overview
• Circuit-centric quantum classifiers
• Building logical qubits in a superconducting quantum computing system
• Quantum machine learning: a classical perspective
• Quantum-enhanced machine learning
• A generative modeling approach for benchmarking and training shallow quantum circuits
• Robust data encodings for quantum classifiers
• An approximate Fourier transform useful in quantum factoring
• Prediction by linear regression on a quantum computer
• Opportunities and challenges for quantum-assisted machine learning in near-term quantum computers
• Addition on a Quantum Computer
• Support vector machines on the D-Wave quantum annealer
• Application of Quantum Annealing to Training of Deep Neural Networks
• Quantum perceptron over a field and neural network architecture selection in a quantum computer
• Supervised learning with a quantum classifier using a multi-level system
• Quantum Computing: An Overview Across the System Stack
• Quantum machine learning with glow for episodic tasks and decision games
• QEML (Quantum Enhanced Machine Learning): Using Quantum Computing to Enhance ML Classifiers and Feature Spaces
• Convolution filter embedded quantum gate autoencoder
• Nonlinear regression based on a hybrid quantum computer
• A Probabilistic framework for Quantum Clustering

Cited by in corpus (8)

• Hybrid quantum convolutional neural networks model for COVID-19 prediction using chest X-Ray images
• Neutral Atom Quantum Computing Hardware: Performance and End-User Perspective
• Variational Quantum Reinforcement Learning via Evolutionary Optimization
• Quantum Architecture Search via Deep Reinforcement Learning
• Hybrid Quantum-Classical Graph Convolutional Network
• Long-lived Particles Anomaly Detection with Parametrized Quantum Circuits
• An Efficient Binary Harris Hawks Optimization based on Quantum SVM for Cancer Classification Tasks
• SEQUENT: Towards Traceable Quantum Machine Learning using Sequential Quantum Enhanced Training