Quantum Computation: An Overview

Springer, 2020

Quantum machine learning is at the intersection of two of the most sought after research areas—quantum computing and classical machine learning. Quantum machine learning investigates how results from the quantum world can be used to solve problems from machine learning. The amount of data needed to reliably train a classical computation model is evergrowing and reaching the limits which normal computing devices can handle. In such a scenario, quantum computation can aid in continuing training with huge data. Quantum machine learning looks to devise learning algorithms faster than their classical counterparts. Classical machine learning is about trying to find patterns in data and using those patterns to predict further events. Quantum systems, on the other hand, produce atypical patterns which are not producible by classical systems, thereby postulating that quantum computers may overtake classical computers on machine learning tasks. Here, we review the previous literature on quant...

Entropy

Despite its undeniable success, classical machine learning remains a resource-intensive process. Practical computational efforts for training state-of-the-art models can now only be handled by high speed computer hardware. As this trend is expected to continue, it should come as no surprise that an increasing number of machine learning researchers are investigating the possible advantages of quantum computing. The scientific literature on Quantum Machine Learning is now enormous, and a review of its current state that can be comprehended without a physics background is necessary. The objective of this study is to present a review of Quantum Machine Learning from the perspective of conventional techniques. Departing from giving a research path from fundamental quantum theory through Quantum Machine Learning algorithms from a computer scientist’s perspective, we discuss a set of basic algorithms for Quantum Machine Learning, which are the fundamental components for Quantum Machine Lea...

arXiv: Quantum Physics, 2018

This text aims to present and explain quantum machine learning algorithms to a data scientist in an accessible and consistent way. The algorithms and equations presented are not written in rigorous mathematical fashion, instead, the pressure is put on examples and step by step explanation of difficult topics. This contribution gives an overview of selected quantum machine learning algorithms, however there is also a method of scores extraction for quantum PCA algorithm proposed as well as a new cost function in feed-forward quantum neural networks is introduced. The text is divided into four parts: the first part explains the basic quantum theory, then quantum computation and quantum computer architecture are explained in section two. The third part presents quantum algorithms which will be used as subroutines in quantum machine learning algorithms. Finally, the fourth section describes quantum machine learning algorithms with the use of knowledge accumulated in previous parts.

2015

Here we discuss advances in the field of quantum machine learning. The following document offers a hybrid discussion; both reviewing the field as it is currently, and suggesting directions for further research. We include both algorithms and experimental implementations in the discussion. The field's outlook is generally positive, showing significant promise. However, we believe there are appreciable hurdles to overcome before one can claim that it is a primary application of quantum computation.

IEEE Access

Encouraged by growing computing power and algorithmic development, machine learning technologies have become powerful tools for a wide variety of application areas, spanning from agriculture to chemistry and natural language processing. The use of quantum systems to process classical data using machine learning algorithms has given rise to an emerging research area, i.e. quantum machine learning. Despite its origins in the processing of classical data, quantum machine learning also explores the use of quantum phenomena for learning systems, the use of quantum computers for learning on quantum data and how machine learning algorithms and software can be formulated and implemented on quantum computers. Quantum machine learning can have a transformational effect on computer science. It may speed up the processing of information well beyond the existing classical speeds. Recent work has seen the development of quantum algorithms that could serve as foundations for machine learning applications. Despite its great promise, there are still significant hardware and software challenges that need to be resolved before quantum machine learning becomes practical. In this paper, we present an overview of quantum machine learning in the light of classical approaches. Departing from foundational concepts of machine learning and quantum computing, we discuss various technical contributions, strengths and similarities of the research work in this domain. We also elaborate upon the recent progress of different quantum machine learning approaches, their complexity, and applications in various fields such as physics, chemistry and natural language processing. INDEX TERMS Quantum machine learning, quantum computing, quantum algorithms, QuBit.

Memoria Investigaciones en Ingenieria, 2024

This article corresponds to an extensive review of Quantum Computers. We chose to consider topics relevant to quantum computing, such as machine learning, and the deepening of other issues related to cybersecurity. We introduce the reader to the basic concepts of quantum computing so that they can easily understand the terms mentioned in this review. We analyze different state of the art articles, and we give a summary of the contributions made. Finally, we conclude with the analysis of the bibliography, the research centers, the current state of the art, surprising results and conclusions.

2017

The aim of the project is to study two of the most widely used machine learning strategies, namely KNearest Neighbours algorithm and Perceptron Learning algorithm, in a quantum setting, and study the speedups that the quantum modules allow over the classical counterparts. The study is primarily based on the following 3 papers: 1. Quantum Perceptron Models, by N. Wiebe, A. Kapoor and K. M. Svore. 2. Quantum Algorithm for K-Nearest Neighbors Classification Based on the Metric of Hamming Distance, by Y. Ruan, X. Xue, H. Liu, J. Tan, and X. Li. 3. Quantum Algorithms for Nearest-Neighbor Methods for Supervised and Unsupervised Learning, by N. Wiebe, A. Kapoor and K. M. Svore.

Quantum Machine Intelligence

The last two decades have seen an explosive growth in the theory and practice of both quantum computing and machine learning. Modern machine learning systems process huge volumes of data and demand massive computational power. As silicon semiconductor miniaturization approaches its physics limits, quantum computing is increasingly being considered to cater to these computational needs in the future. Small-scale quantum computers and quantum annealers have been built and are already being sold commercially. Quantum computers can benefit machine learning research and application across all science and engineering domains. However, owing to its roots in quantum mechanics, research in this field has so far been confined within the purview of the physics community, and most work is not easily accessible to researchers from other disciplines. In this paper, we provide a background and summarize key results of quantum computing before exploring its application to supervised machine learning problems. By eschewing results from physics that have little bearing on quantum computation, we hope to make this introduction accessible to data scientists, machine learning practitioners, and researchers from across disciplines.

Machine learning is now widely used almost everywhere, primarily for forecasting. The main idea of the work is to identify the possibility of achieving a quantum advantage when solving machine learning problems on a quantum computer.

ESANN 2023 proceesdings

Artificial Intelligence (AI), a discipline with decades of history, is living its golden era due to striking developments that solve problems that were unthinkable just a few years ago, like generative models of text, images and video. The broad range of AI applications has also arrived to Physics, providing solutions to bottleneck situations, e.g., numerical methods that could not solve certain problems or took an extremely long time, optimization of quantum experimentation, or qubit control. Besides, Quantum Computing has become extremely popular for speeding up AI calculations, especially in the case of data-driven AI, i.e., Machine Learning (ML). The term Quantum ML is already known and deals with learning in quantum computers or quantum annealers, quantum versions of classical ML models and different learning approaches for quantum measurement and control. Quantum AI (QAI) tries to take a step forward in order to come up with disruptive concepts, such as, human-quantum-computer interfaces, sentiment analysis in quantum computers or explainability of quantum computing calculations, to name a few. This special session includes five high-quality papers on relevant topics, like quantum reinforcement learning, parallelization of quantum calculations, quantum feature selection and quantum vector quantization, thus capturing the richness and variability of approaches within QAI.