Comparison of Two Quantum Nearest Neighbor Classifiers on IBM’s Quantum Simulator

Abstract

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.

Training a Quantum Neural Network to Solve the Contextual Multi-Armed Bandit Problem

“…Making use of the counterintuitive and distinctive properties of superposition, entanglement, and interference of quantum states, quantum computing is a new computing paradigm based on the laws of quantum mechanics. Quantum computers can process information more efficiently than traditional computers and provide us with a platform to enhance classical machine learning algorithms and to develop new quantum learning algorithms [4][5][6][7][8][9][10][11][12][13][14][15].…”

Q Learning with Quantum Neural Networks

“…In the domain of quantum machine learning, RL has received relatively less attention, considering the quantum enhancements in supervised and unsupervised learning [6][7][8][9][10][11][12][13][14][15][16][17][18]. In our previous work [19], we solved the contextual bandit problem with a quantum neural network.…”

Boosted Binary Quantum Classifier via Graphical Kernel

“…With regard to efficient quantum computing, the research of designing quantum classifier has attracted the attention of many researchers. Based on different data logical structures, some researchers have proposed distinct quantum classifiers, such as quantum support vector machine (SVM), decision tree classifiers and K-nearest neighbor (KNN) algorithms [ 4 , 5 , 6 ]. In general, a kernel measures similarity of data is applied to the design of distance-based and swap-test classification protocols in quantum mechanics.Graph state in quantum computation is a kind of essential multiparticle entangled state, which has been intensively researched with abundant results [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ].…”