Quantum machine learning

  title={Quantum machine learning},
  author={Jacob D. Biamonte and Peter Wittek and Nicola Pancotti and Patrick Rebentrost and Nathan Wiebe and Seth Lloyd},
Fuelled by increasing computer power and algorithmic advances, machine learning techniques have become powerful tools for finding patterns in data. Quantum systems produce atypical patterns that classical systems are thought not to produce efficiently, so it is reasonable to postulate that quantum computers may outperform classical computers on machine learning tasks. The field of quantum machine learning explores how to devise and implement quantum software that could enable machine learning… 

Quantum Machine Learning: A Review and Current Status

The previous literature on quantum machine learning is reviewed and the current status of it is provided, postulating that quantum computers may overtake classical computers on machine learning tasks.

Advances of Quantum Machine Learning

This chapter provides quantum computation, advance of QML techniques, QML kernel space and optimization, and future work ofQML.

Quantum Driven Machine Learning

A quantum machine learning model based on quantum support vector machine (QSVM) algorithm to solve a classification problem and the results are indicative that quantum computers offer quantum speed-up.

An Introduction to Quantum Machine Learning Algorithms

Typical ideas and methods of quantum machine learning are introduced to show how quantum algorithms improve the performance of machine learning process.

Quantum Computing Methods for Supervised Learning

This paper provides a background and summarize key results of quantum computing before exploring its application to supervised machine learning problems, and aims to make this introduction accessible to data scientists, machine learning practitioners, and researchers from across disciplines.

Recent Progress in Quantum Machine Learning

The objective of this chapter is to facilitate the reader to grasp the key components involved in the field to be able to understand the essentialities of the subject and thus can compare computations of quantum computing with its counterpart classical machine learning algorithms.

Recent advances in quantum machine learning

The state-of-the-art research of algorithms of quantummachine learning is reviewed and a path of the research from the basic quantum information to quantum machine learning algorithms is shown from the perspective of people in the field of computer science.

Opportunities in Quantum Reservoir Computing and Extreme Learning Machines

In this review article, recent proposals and first experiments displaying a broad range of possibilities are reviewed when quantum inputs, quantum physical substrates and quantum tasks are considered.

QEML (Quantum Enhanced Machine Learning): Using Quantum Computing to Enhance ML Classifiers and Feature Spaces

This paper first understands the mathematical intuition for the implementation of quantum feature space and successfully simulates quantum properties and algorithms like Fidelity and Grover's Algorithm via the Qiskit python library and the IBM Quantum Experience platform.

Experimental Machine Learning of Quantum States.

It is shown that it is possible to experimentally train an artificial neural network to efficiently learn and classify quantum states, without the need of obtaining the full information of the states, and shed new light on how classification of quantum states can be achieved with limited resources.



Quantum Machine Learning over Infinite Dimensions.

This work presents the critical subroutines of quantum machine learning algorithms for an all-photonic continuous-variable quantum computer that can lead to exponential speedups in situations where classical algorithms scale polynomially.

An introduction to quantum machine learning

This contribution gives a systematic overview of the emerging field of quantum machine learning and presents the approaches as well as technical details in an accessible way, and discusses the potential of a future theory of quantum learning.

Entanglement-based machine learning on a quantum computer.

The first experimental entanglement-based classification of two-, four-, and eight-dimensional vectors to different clusters using a small-scale photonic quantum computer is reported, which can be scaled to larger numbers of qubits, and may provide a new route to accelerate machine learning.

Harnessing disordered quantum dynamics for machine learning

A novel platform, quantum reservoir computing, is proposed to solve issues successfully by exploiting natural quantum dynamics, which is ubiquitous in laboratories nowadays, for machine learning by exploiting nonlinear dynamics including classical chaos.

Quantum deep learning

It is shown that quantum computing not only reduces the time required to train a deep restricted Boltzmann machine, but also provides a richer and more comprehensive framework for deep learning than classical computing and leads to significant improvements in the optimization of the underlying objective function.

Supervised Quantum Learning without Measurements

A quantum machine learning algorithm for efficiently solving a class of problems encoded in quantum controlled unitary operations that introduces feedback in the dynamics and eliminates the necessity of intermediate measurements using a quantum time-delayed equation.

Quantum Machine Learning without Measurements

A quantum machine learning algorithm for efficiently solving a class of problems encoded in quantum controlled unitary operations based on a sequentially-applied time-delayed equation that allows one to implement feedback-driven dynamics without the need of intermediate measurements.

Quantum algorithms for supervised and unsupervised machine learning

Machine-learning tasks frequently involve problems of manipulating and classifying large numbers of vectors in high-dimensional spaces. Classical algorithms for solving such problems typically take

Experimental realization of a quantum support vector machine.

A quantum machine learning algorithm is demonstrated to implement handwriting recognition on a four-qubit NMR test bench and learns standard character fonts and then recognizes handwritten characters from a set with two candidates.

Quantum machine learning with small-scale devices: Implementing a distance-based classifier with a quantum interference circuit

A distance-based classifier that is realised by a simple quantum interference circuit that computes the distance between data points in quantum parallel and is demonstrated using the IBM Quantum Experience and analysed with numerical simulations.