Today computers are used to store data in memory and then process them. In our big data era, we are facing the challenge of storing and processing the data simply due to their fast ever growing size. Quantum computati...Today computers are used to store data in memory and then process them. In our big data era, we are facing the challenge of storing and processing the data simply due to their fast ever growing size. Quantum computation offers solutions to these two prominent issues quantum mechanically and beautifully. Through careful design to employ superposition, entanglement, and interference of quantum states, a quantum algorithm can allow a quantum computer to store datasets of exponentially large size as linear size and then process them in parallel. Quantum computing has found its way in the world of machine learning where new ideas and approaches are in great need as the classical computers have reached their capacity and the demand for processing big data grows much faster than the computing power the classical computers can provide today. Nearest neighbor algorithms are simple, robust, and versatile supervised machine learning algorithms, which store all training data points as their learned “model” and make the prediction of a new test data point by computing the distances between the query point and all the training data points. Quantum counterparts of these classical algorithms provide efficient and elegant ways to deal with the two major issues of storing data in memory and computing the distances. The purpose of our study is to select two similar quantum nearest neighbor algorithms and use a simple dataset to give insight into how they work, highlight their quantum nature, and compare their performances on IBM’s quantum simulator.展开更多
This is a review of quantum methods for machine learning problems that consists of two parts.The first part,"quantum tools",presented some of the fundamentals and introduced several quantum tools based on kn...This is a review of quantum methods for machine learning problems that consists of two parts.The first part,"quantum tools",presented some of the fundamentals and introduced several quantum tools based on known quantum search algorithms.This second part of the review presents several classification problems in machine learning that can be accelerated with quantum subroutines.We have chosen supervised learning tasks as typical classification problems to illustrate the use of quantum methods for classification.展开更多
文摘Today computers are used to store data in memory and then process them. In our big data era, we are facing the challenge of storing and processing the data simply due to their fast ever growing size. Quantum computation offers solutions to these two prominent issues quantum mechanically and beautifully. Through careful design to employ superposition, entanglement, and interference of quantum states, a quantum algorithm can allow a quantum computer to store datasets of exponentially large size as linear size and then process them in parallel. Quantum computing has found its way in the world of machine learning where new ideas and approaches are in great need as the classical computers have reached their capacity and the demand for processing big data grows much faster than the computing power the classical computers can provide today. Nearest neighbor algorithms are simple, robust, and versatile supervised machine learning algorithms, which store all training data points as their learned “model” and make the prediction of a new test data point by computing the distances between the query point and all the training data points. Quantum counterparts of these classical algorithms provide efficient and elegant ways to deal with the two major issues of storing data in memory and computing the distances. The purpose of our study is to select two similar quantum nearest neighbor algorithms and use a simple dataset to give insight into how they work, highlight their quantum nature, and compare their performances on IBM’s quantum simulator.
基金supported in part by the Russian Science Foundation(No.19-19-00656)the Natural Science Foundation of Guangdong Province,China(No.2019A1515011721).
文摘This is a review of quantum methods for machine learning problems that consists of two parts.The first part,"quantum tools",presented some of the fundamentals and introduced several quantum tools based on known quantum search algorithms.This second part of the review presents several classification problems in machine learning that can be accelerated with quantum subroutines.We have chosen supervised learning tasks as typical classification problems to illustrate the use of quantum methods for classification.
