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Quantitative Evaluation of Systems: 19th International Conference, QEST 2022, Warsaw, Poland, September 12–16, 2022, Proceedings (Lecture Notes in Computer Science #13479)
by Erika Ábrahám Marco PaolieriThis book constitutes the proceedings of the 19th International Conference on Quantitative Evaluation Systems, QEST 2022, held in Warsaw, Poland, in September 2022.The 19 full papers presented together with 1 keynote paper were carefully reviewed and selected from 44 submissions. The papers are organized in the following topics: program analysis; parameter synthesis; markovian agents and population models; dynamical systems; tools; applications and automata theory; and applications.
Quantitative Evaluation of Systems: 20th International Conference, QEST 2023, Antwerp, Belgium, September 20–22, 2023, Proceedings (Lecture Notes in Computer Science #14287)
by Nils Jansen Mirco TribastoneThis book constitutes the proceedings of the 20th International Conference on Quantitative Evaluation of Systems, QEST 2023, which took place in Antwerp, Belgium, in September 2023. The 23 papers included in this book were carefully reviewed and selected from 44 submissions. They deal with current topics in quantitative evaluation and verification of computer systems and networks, focusing on data-driven and machine-learning systems, case studies, and tool papers. The book also contains the extended abstract of the invited talk from David Parker.
Quantitative Genetics
by Shizhong XuThe intended audience of this textbook are plant and animal breeders, upper-level undergraduate and graduate students in biological and agricultural science majors. Statisticians who are interested in understanding how statistical methods are applied to genetics and agriculture can benefit substantially by reading this book. One characteristic of this textbook is represented by three chapters of technical reviews for Mendelian genetics, population genetics and preliminary statistics, which are prerequisites for studying quantitative genetics. Numerous examples are provided to illustrate different methods of data analysis and estimation of genetic parameters. Along with each example of data analyses is the program code of SAS (statistical analysis system).
Quantitative Graph Theory: Mathematical Foundations and Applications
by Matthias Dehmer Frank Emmert-StreibThe first book devoted exclusively to quantitative graph theory, Quantitative Graph Theory: Mathematical Foundations and Applications presents and demonstrates existing and novel methods for analyzing graphs quantitatively. Incorporating interdisciplinary knowledge from graph theory, information theory, measurement theory, and statistical technique
Quantitative Logic and Soft Computing 2016
by Tai-He Fan Shui-Li Chen San-Min Wang Yong-Ming LiThis book is the proceedings of the Fourth International Conference on Quantitative Logic and Soft Computing (QLSC2016) held 14-17, October, 2016 in Zhejiang Sci-Tech University, Hangzhou, China. It includes 61 papers, of which 5 are plenary talks( 3 abstracts and 2 full length talks). QLSC2016 was the fourth in a series of conferences on Quantitative Logic and Soft Computing. This conference was a major symposium for scientists, engineers and practitioners to present their updated results, ideas, developments and applications in all areas of quantitative logic and soft computing. The book aims to strengthen relations between industry research laboratories and universities in fields such as quantitative logic and soft computing worldwide as follows: (1) Quantitative Logic and Uncertainty Logic; (2) Automata and Quantification of Software; (3) Fuzzy Connectives and Fuzzy Reasoning; (4) Fuzzy Logical Algebras; (5) Artificial Intelligence and Soft Computing; (6) Fuzzy Sets Theory and Applications.
Quantitative Methods for the Social Sciences: A Practical Introduction with Examples in R (Springer Texts in Political Science and International Relations)
by Daniel Stockemer Jean-Nicolas BordeleauThis textbook offers an essential introduction to survey research and quantitative methods with clear instructions on how to conduct statistical tests with R. Building on the premise that we need to teach statistical methods in a holistic and practical format, the book guides students through the four main elements of survey research and quantitative analysis: (1) the importance of survey research, (2) preparing a survey, (3) conducting a survey and (4) analyzing a survey. In detail, students will learn how to create their own questionnaire on the basis of formulating hypotheses; sampling participants; disseminating their questionnaire; creating datasets; and analyzing their data. The data analytical sections of this revised and extended edition explain the theory, rationale and mathematical foundations of relevant bivariate and multi-variate statistical tests. These include the T-test, F-test, Chi-square test and correlation analyses, as well as bivariate and multivariate regression analyses. In addition, the book offers a brief introduction to statistical computing with R, which includes clear instructions on how to conduct these statistical tests in R. Given the breadth of its coverage, the textbook is suitable for introductory statistics, survey research and quantitative methods classes in the social sciences.
Quantitative Methods for the Social Sciences: A Practical Introduction with Examples in SPSS and Stata
by Daniel StockemerThis textbook offers an essential introduction to survey research and quantitative methods. Building on the premise that statistical methods need to be learned in a practical fashion, the book guides students through the various steps of the survey research process and helps to apply those steps toward a real example. In detail, the textbook introduces students to the four pillars of survey research and quantitative analysis: (1) the importance of survey research, (2) preparing a survey, (3) conducting a survey and (4) analyzing a survey. Students are shown how to create their own questionnaire based on some theoretically derived hypotheses to achieve empirical findings for a solid dataset. Lastly, they use said data to test their hypotheses in a bivariate and multivariate realm. The book explains the theory, rationale and mathematical foundations of these tests. In addition, it provides clear instructions on how to conduct the tests in SPSS and Stata. Given the breadth of its coverage, the textbook is suitable for introductory statistics, survey research or quantitative methods classes in the social sciences.
Quantitative Methods in Pharmaceutical Research and Development: Concepts and Applications
by Olga V. Marchenko Natallia V. KatenkaThis contributed volume presents an overview of concepts, methods, and applications used in several quantitative areas of drug research, development, and marketing. Chapters bring together the theories and applications of various disciplines, allowing readers to learn more about quantitative fields, and to better recognize the differences between them. Because it provides a thorough overview, this will serve as a self-contained resource for readers interested in the pharmaceutical industry, and the quantitative methods that serve as its foundation. Specific disciplines covered include:BiostatisticsPharmacometricsGenomicsBioinformaticsPharmacoepidemiologyCommercial analyticsOperational analyticsQuantitative Methods in Pharmaceutical Research and Development is ideal for undergraduate students interested in learning about real-world applications of quantitative methods, and the potential career options open to them. It will also be of interest to experts working in these areas.
Quantitative Methods in Supply Chain Management
by Ioannis T. ChristouQuantitative Methods in Supply Chain Management presents some of the most important methods and tools available for modeling and solving problems arising in the context of supply chain management. In the context of this book, "solving problems" usually means designing efficient algorithms for obtaining high-quality solutions. The first chapter is an extensive optimization review covering continuous unconstrained and constrained linear and nonlinear optimization algorithms, as well as dynamic programming and discrete optimization exact methods and heuristics. The second chapter presents time-series forecasting methods together with prediction market techniques for demand forecasting of new products and services. The third chapter details models and algorithms for planning and scheduling with an emphasis on production planning and personnel scheduling. The fourth chapter presents deterministic and stochastic models for inventory control with a detailed analysis on periodic review systems and algorithmic development for optimal control of such systems. The fifth chapter discusses models and algorithms for location/allocation problems arising in supply chain management, and transportation problems arising in distribution management in particular, such as the vehicle routing problem and others. The sixth and final chapter presents a short list of new trends in supply chain management with a discussion of the related challenges that each new trend might bring along in the immediate to near future. Overall, Quantitative Methods in Supply Chain Management may be of particular interest to students and researchers in the fields of supply chain management, operations management, operations research, industrial engineering, and computer science.
Quantitative Models for Microscopic to Macroscopic Biological Macromolecules and Tissues
by Luis Olivares-Quiroz Osbaldo Resendis-AntonioThis book presents cutting-edge research on the use of physical and mathematical formalisms to model and quantitatively analyze biological phenomena ranging from microscopic to macroscopic systems. The systems discussed in this compilation cover protein folding pathways, gene regulation in prostate cancer, quorum sensing in bacteria to mathematical and physical descriptions to analyze anomalous diffusion in patchy environments and the physical mechanisms that drive active motion in large sets of particles, both fundamental descriptions that can be applied to different phenomena in biology. All chapters are written by well-known experts on their respective research fields with a vast amount of scientific discussion and references in order the interested reader can pursue a further reading. Given these features, we consider Quantitative Models for Microscopic to Macroscopic Biological Macromolecules and Tissues as an excellent and up-to-date resource and reference for advanced undergraduate students, graduate students and junior researchers interested in the latest developments at the intersection of physics, mathematics, molecular biology, and computational sciences. Such research field, without hesitation, is one of the most interesting, challenging and active of this century and the next.
Quantitative Parameterization and 3D-run-out Modelling of Rockfalls at Steep Limestone Cliffs in the Bavarian Alps
by Bettina SellmeierThis pioneering work deals with the parameterization of rockfalls in the context of 3D run-out modelling at a study site in the Bavarian Alps. The main objective was to cover not only low-magnitude, high-frequency rockfalls (
Quantitative Portfolio Management: with Applications in Python (Springer Texts in Business and Economics)
by Pierre BrugièreThis self-contained book presents the main techniques of quantitative portfolio management and associated statistical methods in a very didactic and structured way, in a minimum number of pages. The concepts of investment portfolios, self-financing portfolios and absence of arbitrage opportunities are extensively used and enable the translation of all the mathematical concepts in an easily interpretable way. All the results, tested with Python programs, are demonstrated rigorously, often using geometric approaches for optimization problems and intrinsic approaches for statistical methods, leading to unusually short and elegant proofs. The statistical methods concern both parametric and non-parametric estimators and, to estimate the factors of a model, principal component analysis is explained. The presented Python code and web scraping techniques also make it possible to test the presented concepts on market data. This book will be useful for teaching Masters students and for professionals in asset management, and will be of interest to academics who want to explore a field in which they are not specialists. The ideal pre-requisites consist of undergraduate probability and statistics and a familiarity with linear algebra and matrix manipulation. Those who want to run the code will have to install Python on their pc, or alternatively can use Google Colab on the cloud. Professionals will need to have a quantitative background, being either portfolio managers or risk managers, or potentially quants wanting to double check their understanding of the subject.
Quantitative Social Science: An Introduction
by Kosuke ImaiAn introductory textbook on data analysis and statistics written especially for students in the social sciences and allied fields Quantitative analysis is an increasingly essential skill for social science research, yet students in the social sciences and related areas typically receive little training in it--or if they do, they usually end up in statistics classes that offer few insights into their field. This textbook is a practical introduction to data analysis and statistics written especially for undergraduates and beginning graduate students in the social sciences and allied fields, such as economics, sociology, public policy, and data science. Quantitative Social Science engages directly with empirical analysis, showing students how to analyze data using the R programming language and to interpret the results--it encourages hands-on learning, not paper-and-pencil statistics. More than forty data sets taken directly from leading quantitative social science research illustrate how data analysis can be used to answer important questions about society and human behavior. Proven in the classroom, this one-of-a-kind textbook features numerous additional data analysis exercises and interactive R programming exercises, and also comes with supplementary teaching materials for instructors. Written especially for students in the social sciences and allied fields, including economics, sociology, public policy, and data science Provides hands-on instruction using R programming, not paper-and-pencil statistics Includes more than forty data sets from actual research for students to test their skills on Covers data analysis concepts such as causality, measurement, and prediction, as well as probability and statistical tools Features a wealth of supplementary exercises, including additional data analysis exercises and interactive programming exercises Offers a solid foundation for further study Comes with additional course materials online, including notes, sample code, exercises and problem sets with solutions, and lecture slides
Quantitative Social Science: An Introduction in tidyverse
by Kosuke Imai Nora Webb WilliamsA tidyverse edition of the acclaimed textbook on data analysis and statistics for the social sciences and allied fieldsQuantitative analysis is an essential skill for social science research, yet students in the social sciences and related areas typically receive little training in it. Quantitative Social Science is a practical introduction to data analysis and statistics written especially for undergraduates and beginning graduate students in the social sciences and allied fields, including business, economics, education, political science, psychology, sociology, public policy, and data science. Proven in classrooms around the world, this one-of-a-kind textbook engages directly with empirical analysis, showing students how to analyze and interpret data using the tidyverse family of R packages. Data sets taken directly from leading quantitative social science research illustrate how to use data analysis to answer important questions about society and human behavior.Emphasizes hands-on learning, not paper-and-pencil statisticsIncludes data sets from actual research for students to test their skills onCovers data analysis concepts such as causality, measurement, and prediction, as well as probability and statistical toolsFeatures a wealth of supplementary exercises, including additional data analysis exercises and programming exercisesOffers a solid foundation for further studyComes with additional course materials online, including notes, sample code, exercises and problem sets with solutions, and lecture slides
Quantitative Sustainability: Interdisciplinary Research for Sustainable Development Goals
by Nicola Casagli Stefano Fantoni Cosimo Solidoro Marina CobalThis open access book focuses on how scientific methodologies can help industrial managers, entrepreneurs and policymakers handle the 17 Sustainable Development Goals in an efficient and realistic way. It also offers an operative scheme for scientists to overcome their discipline barriers. Is interdisciplinarity an intrinsic research value or is it merely instrumental for handling the increasing flux of open problems that sustainability poses to science?Can these problems of sustainability be solved with what the authors already know? Is it just a matter of having the right people at the table and giving them sufficient resources, or is it something more? Is meeting the needs of the present without compromising those of future generations a scientific definition of sustainable development? Questions similar to those posed in the sixties regarding complexity must be asked about sustainability today. In addition, the new data science includes powerful tools for making novel quantitative predictions about future sustainability indicators, an open problem that the book discusses. This book is primarily addressed to Ph.D. students, postdocs and senior researchers in the Life and Hard Science (LHS) and Social Sciences and Humanities (SSH) disciplines, as well as professionals of the primary, secondary and tertiary industrial sectors.
Quantitative Trading Strategies Using Python: Technical Analysis, Statistical Testing, and Machine Learning
by Peng LiuBuild and implement trading strategies using Python. This book will introduce you to the fundamental concepts of quantitative trading and shows how to use Python and popular libraries to build trading models and strategies from scratch. It covers practical trading strategies coupled with step-by-step implementations that touch upon a wide range of topics, including data analysis and visualization, algorithmic trading, backtesting, risk management, optimization, and machine learning, all coupled with practical examples in Python.Part one of Quantitative Trading Strategies with Python covers the fundamentals of trading strategies, including an introduction to quantitative trading, the electronic market, risk and return, and forward and futures contracts. Part two introduces common trading strategies, including trend-following, momentum trading, and evaluation process via backtesting. Part three covers more advanced topics, including statistical arbitrage using hypothesis testing, optimizing trading parameters using Bayesian optimization, and generating trading signals using a machine learning approach. Whether you're an experienced trader looking to automate your trading strategies or a beginner interested in learning quantitative trading, this book will be a valuable resource. Written in a clear and concise style that makes complex topics easy to understand, and chock full of examples and exercises to help reinforce the key concepts, you’ll come away from it with a firm understanding of core trading strategies and how to use Python to implement them.What You Will LearnMaster the fundamental concepts of quantitative tradingUse Python and its popular libraries to build trading models and strategies from scratchPerform data analysis and visualization, algorithmic trading, backtesting, risk management, optimization, and machine learning for trading strategies using PythonUtilize common trading strategies such as trend-following, momentum trading, and pairs tradingEvaluate different quantitative trading strategies by applying the relevant performance measures and statistics in a scientific manner during backtestingWho This Book Is ForAspiring quantitative traders and analysts, data scientists interested in finance, and researchers or students studying quantitative finance, financial engineering, or related fields.
Quantitative Ultrasound in Soft Tissues
by Jonathan Mamou Michael L. OelzeDue to parallel advances in signal processing and computer hardware in the last 15 years, quantitative ultrasound techniques have reached maturity, allowing for the construction of quantitative maps or images of soft tissues. This book will focus on 5 modern research topics related to quantitative ultrasound of soft tissues: - Spectral-based methods for tissue characterization, tissue typing, cancer detection, etc. ; - Envelope statistics analysis as a means of quantifying and imaging tissue properties; - Ultrasound elastography for quantifying elastic properties of tissues (several clinical ultrasound scanners now display elastography images); - Scanning acoustic microscopy for forming images of mechanical properties of soft tissues with micron resolution (desktop size scanners are now available); and - Ultrasound computer tomography for breast cancer imaging (new ultrasound tomography systems have been developed and are currently under evaluation clinically).
Quantitative User Experience Research: Informing Product Decisions by Understanding Users at Scale
by Chris Chapman Kerry RoddenThis book is your definitive guide to the rapidly growing role of Quantitative User Experience (Quant UX) Research in product development. The book provides an overview of the skills you need on the job, presents hands-on projects with reusable code, and shares advice on starting and developing a career. The book goes beyond basic skills to focus on what is unique to Quant UX. The authors are two of the most widely recognized practitioners in Quant UX research, and this book shares insights from their combined decades of experience. Organizations today have more data about user needs and behaviors than ever before. With this large-scale data, Quant UX researchers work to understand usage patterns, measure the impact of design changes, and inform strategic decisions. In the Quant UX role, interdisciplinary researchers apply analytical skills to uncover user needs, inform engineering and design, answer strategic business questions, and optimize software and hardware products for human interaction. This book provides guidance around customer satisfaction surveys, understanding user behavior from log analysis, and the statistical methods that are commonly used to assess user outcomes. What You Will LearnDiscover the role of Quantitative User Experience (Quant UX) researchUnderstand how Quant UX research differs from other disciplines such as data sciencePlan common research projects and know how to achieve successPosition Quant UX activities in product development, engineering, and UX organizationsApply the HEART framework to measure user experience outcomesEvaluate your skills and potential to be hired as a Quant UX researcherKnow what to expect during job interviewsFind examples of common Quant UX projects with shared R code and data setsWho This Book Is ForPractitioners and managers who seek a comprehensive guide to the new field of Quantitative User Experience Research. Readers will understand the Quant UX role, build research skills, find examples of hands-on code and analyses, learn about UX organizations and stakeholders, and receive advice on job interviews and career paths. Data scientists, social scientists, and other researchers will learn how their skills transfer to Quant UX, where they can help teams build better, more successful products.
Quantitatives Entwicklungsmanagement
by Axel Hahn Stephan Große Austing Stefan HäuslerDie Produktentwicklung ist der dynamischste und am schwersten zu beherrschende Prozess in Industrieunternehmen. Prozessreifegradmodelle wie CMMI erfordern dazu quantitative Methoden als Werkzeug des Entwicklungsmanagements. Mit der modellgetriebenen Entwicklung steht eine Datenbasis für eine durchgehende quantitative Analyse zur Verfügung. Modellanalysen erlauben eine prozessbegleitende Qualitätsmessung und die Bestimmung der Produktkomplexität und des Entwicklungsfortschritts. Das Buch stellt bestehende Methoden des quantitativen Entwicklungsmanagements zur Entscheidungsunterstützung auf operativer und strategischer Ebene vor. Die Autoren zeigen, wie die modellgetriebene Entwicklung zur Analyse der erreichten Ergebnisse genutzt werden kann, etwa zur Bewertung von Methoden, Werkzeugen sowie zur Prozess- oder Projektanpassung.
Quantized Phenomena of Transport and Magneto-Optics in Magnetic Topological Insulator Heterostructures (Springer Theses)
by Masataka MogiThis book presents experimental studies on emergent transport and magneto-optical properties in three-dimensional topological insulators with two-dimensional Dirac fermions on their surfaces. Designing magnetic heterostructures utilizing a cutting-edge growth technique (molecular beam epitaxy) stabilizes and manifests new quantization phenomena, as confirmed by low-temperature electrical transport and time-domain terahertz magneto-optical measurements. Starting with a review of the theoretical background and recent experimental advances in topological insulators in terms of a novel magneto-electric coupling, the author subsequently explores their magnetic quantum properties and reveals topological phase transitions between quantum anomalous Hall insulator and trivial insulator phases; a new topological phase (the axion insulator); and a half-integer quantum Hall state associated with the quantum parity anomaly. Furthermore, the author shows how these quantum phases can be significantly stabilized via magnetic modulation doping and proximity coupling with a normal ferromagnetic insulator. These findings provide a basis for future technologies such as ultra-low energy consumption electronic devices and fault-tolerant topological quantum computers.
Quantum Adaptivity in Biology: From Genetics to Cognition
by Andrei Khrennikov Masanari Asano Masanori Ohya Yoshiharu Tanaka Ichiro YamatoThis book examines information processing performed by bio-systems at all scales: from genomes, cells and proteins to cognitive and even social systems. It introduces a theoretical/conceptual principle based on quantum information and non-Kolmogorov probability theory to explain information processing phenomena in biology as a whole. The book begins with an introduction followed by two chapters devoted to fundamentals, one covering classical and quantum probability, which also contains a brief introduction to quantum formalism, and another on an information approach to molecular biology, genetics and epigenetics. It then goes on to examine adaptive dynamics, including applications to biology, and non-Kolmogorov probability theory. Next, the book discusses the possibility to apply the quantum formalism to model biological evolution, especially at the cellular level: genetic and epigenetic evolutions. It also presents a model of the epigenetic cellular evolution based on the mathematical formalism of open quantum systems. The last two chapters of the book explore foundational problems of quantum mechanics and demonstrate the power of usage of positive operator valued measures (POVMs) in biological science. This book will appeal to a diverse group of readers including experts in biology, cognitive science, decision making, sociology, psychology, and physics; mathematicians working on problems of quantum probability and information and researchers in quantum foundations.
Quantum Algorithms for Cryptographically Significant Boolean Functions: An IBMQ Experience (SpringerBriefs in Computer Science)
by Subhamoy Maitra Tharrmashastha SAPV Debajyoti Bera Arpita MaitraThis book is a timely report of the state-of-the-art analytical techniques in the domain of quantum algorithms related to Boolean functions. It bridges the gap between recent developments in the area and the hands-on analysis of the spectral properties of Boolean functions from a cryptologic viewpoint. Topics covered in the book include Qubit, Deutsch–Jozsa and Walsh spectrum, Grover’s algorithm, Simon’s algorithm and autocorrelation spectrum. The book aims at encouraging readers to design and implement practical algorithms related to Boolean functions. Apart from combinatorial techniques, this book considers implementing related programs in a quantum computer. Researchers, practitioners and educators will find this book valuable.
Quantum Algorithms via Linear Algebra: A Primer
by Richard J. Lipton Kenneth W. ReganQuantum computing explained in terms of elementary linear algebra, emphasizing computation and algorithms and requiring no background in physics.This introduction to quantum algorithms is concise but comprehensive, covering many key algorithms. It is mathematically rigorous but requires minimal background and assumes no knowledge of quantum theory or quantum mechanics. The book explains quantum computation in terms of elementary linear algebra; it assumes the reader will have some familiarity with vectors, matrices, and their basic properties, but offers a review of all the relevant material from linear algebra. By emphasizing computation and algorithms rather than physics, this primer makes quantum algorithms accessible to students and researchers in computer science without the complications of quantum mechanical notation, physical concepts, and philosophical issues.After explaining the development of quantum operations and computations based on linear algebra, the book presents the major quantum algorithms, from seminal algorithms by Deutsch, Jozsa, and Simon through Shor's and Grover's algorithms to recent quantum walks. It covers quantum gates, computational complexity, and some graph theory. Mathematical proofs are generally short and straightforward; quantum circuits and gates are used to illuminate linear algebra; and the discussion of complexity is anchored in computational problems rather than machine models. Quantum Algorithms via Linear Algebra is suitable for classroom use or as a reference for computer scientists and mathematicians.
Quantum Arrangements: Contributions in Honor of Michael Horne (Fundamental Theories of Physics #203)
by David Simon Gregg Jaeger Anton Zeilinger Alexander V. Sergienko Daniel GreenbergerThis book presents a collection of novel contributions and reviews by renowned researchers in the foundations of quantum physics, quantum optics, and neutron physics. It is published in honor of Michael Horne, whose exceptionally clear and groundbreaking work in the foundations of quantum mechanics and interferometry, both of photons and of neutrons, has provided penetrating insight into the implications of modern physics for our understanding of the physical world. He is perhaps best known for the Clauser-Horne-Shimony-Holt (CHSH) inequality. This collection includes an oral history of Michael Horne's contributions to the foundations of physics and his connections to other eminent figures in the history of the subject, among them Clifford Shull and Abner Shimony.
Quantum Artificial Intelligence with Qiskit
by Andreas WichertQuantum Artificial Intelligence (QAI) is a new interdisciplinary research field that combines quantum computing with Artificial Intelligence (AI), aiming to use the unique properties of quantum computers to enhance the capabilities of AI systems. Quantum Artificial Intelligence with Qiskit provides a cohesive overview of the field of QAI, providing the tools for readers to create and manipulate quantum programs on devices as accessible as a laptop computer. Introducing symbolical quantum algorithms, sub-symbolical quantum algorithms, and quantum Machine Learning (ML) algorithms, this book explains each process step by step with associated Qiskit listings. All examples are additionally available for download at https://github.com/andrzejwichert/qai. Allowing readers to learn the basic concepts of quantum computing on their home computers, this book is accessible to both the general readership as well as students and instructors of courses relating to computer science and AI.