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Author: John Parks Le Tellier Publisher: Corwin Press ISBN: 1483362248 Category : Education Languages : en Pages : 233
Book Description
Using evidence-based strategies, this inspiring handbook provides principals and teachers with a catalog of practiced "moves," or actions that upgrade performance, create durable motivation, and renew energy levels.
Author: Bobbi DePorter Publisher: Pearson ISBN: Category : Education Languages : en Pages : 252
Book Description
Now there's a better way to teach anything to anybody! Announcing...Quantum Teaching: Orchestrating Student Success Based on 18 years experience and research with over 25,000 students. Boosts teachers' ability to inspire and students' ability to achieve. This body of knowledge and methodology was first used at SuperCamp, an accelerated Quantum Learning program that achieved outstanding results for students. Quantum Teaching shows teachers how to orchestrate their students' success by taking into account everything in the classroom along with the environment, the design of the curriculum, and how it's presented. The result: a highly-effective way to teach anything to anybody!Available as an illustrated how-to book that bridges the gap between theory and practice and that covers today's hottest topics, like multiple intelligences, this book provides specific, easy-to-follow guidelines for creating more-effective learning environments, better ways to design curricula, and more interesting ways to deliver content and facilitate the learning process. Designed and written as an interactive tool, Quantum Teaching includes lesson planning guidelines to help teachers cover all the bases, without having to culminate different theories or refer to different source materials. A reproducible lesson planning guide makes it easy to start implementing new strategies immediately. Bobbi DePorter, author of the best-selling books Quantum Learning and Quantum Business, is founder and president of Learning Forum, which has helped over 25,000 students of all ages. Mark Reardon, a former teacher and principal, is an internationally recognized lead facilitator for Learning Forum. Sarah Singer-Nouri is an award-winning teacher and trainer.
Author: Maria Schuld Publisher: Springer ISBN: 3319964240 Category : Science Languages : en Pages : 293
Book Description
Quantum machine learning investigates how quantum computers can be used for data-driven prediction and decision making. The books summarises and conceptualises ideas of this relatively young discipline for an audience of computer scientists and physicists from a graduate level upwards. It aims at providing a starting point for those new to the field, showcasing a toy example of a quantum machine learning algorithm and providing a detailed introduction of the two parent disciplines. For more advanced readers, the book discusses topics such as data encoding into quantum states, quantum algorithms and routines for inference and optimisation, as well as the construction and analysis of genuine ``quantum learning models''. A special focus lies on supervised learning, and applications for near-term quantum devices.
Author: Conrad P. Pritscher Publisher: Rodopi ISBN: 9789042013872 Category : Education Languages : en Pages : 220
Book Description
This book shows quantum learning is the resource that unites parts into wholes and then wholes into continually larger wholes. Just as quantum computers can regard sub-atomic particles as a wave and as particles, quantum learning can understand learners as simultaneously nondual (whole) and dual (part). The study includes a reconsideration of clarity in expression and thought
Author: Maria Schuld Publisher: Springer Nature ISBN: 3030830985 Category : Science Languages : en Pages : 321
Book Description
This book offers an introduction into quantum machine learning research, covering approaches that range from "near-term" to fault-tolerant quantum machine learning algorithms, and from theoretical to practical techniques that help us understand how quantum computers can learn from data. Among the topics discussed are parameterized quantum circuits, hybrid optimization, data encoding, quantum feature maps and kernel methods, quantum learning theory, as well as quantum neural networks. The book aims at an audience of computer scientists and physicists at the graduate level onwards. The second edition extends the material beyond supervised learning and puts a special focus on the developments in near-term quantum machine learning seen over the past few years.
Author: Peter Wittek Publisher: Academic Press ISBN: 0128010991 Category : Computers Languages : en Pages : 176
Book Description
Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it focuses on providing a synthesis that explains the most important machine learning algorithms in a quantum framework. Theoretical advances in quantum computing are hard to follow for computer scientists, and sometimes even for researchers involved in the field. The lack of a step-by-step guide hampers the broader understanding of this emergent interdisciplinary body of research. Quantum Machine Learning sets the scene for a deeper understanding of the subject for readers of different backgrounds. The author has carefully constructed a clear comparison of classical learning algorithms and their quantum counterparts, thus making differences in computational complexity and learning performance apparent. This book synthesizes of a broad array of research into a manageable and concise presentation, with practical examples and applications. Bridges the gap between abstract developments in quantum computing with the applied research on machine learning Provides the theoretical minimum of machine learning, quantum mechanics, and quantum computing Gives step-by-step guidance to a broader understanding of this emergent interdisciplinary body of research
Author: Sarah C. Kaiser Publisher: Simon and Schuster ISBN: 1638350906 Category : Computers Languages : en Pages : 545
Book Description
Learn Quantum Computing with Python and Q# introduces quantum computing from a practical perspective. Summary Learn Quantum Computing with Python and Q# demystifies quantum computing. Using Python and the new quantum programming language Q#, you’ll build your own quantum simulator and apply quantum programming techniques to real-world examples including cryptography and chemical analysis. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Quantum computers present a radical leap in speed and computing power. Improved scientific simulations and new frontiers in cryptography that are impossible with classical computing may soon be in reach. Microsoft’s Quantum Development Kit and the Q# language give you the tools to experiment with quantum computing without knowing advanced math or theoretical physics. About the book Learn Quantum Computing with Python and Q# introduces quantum computing from a practical perspective. Use Python to build your own quantum simulator and take advantage of Microsoft’s open source tools to fine-tune quantum algorithms. The authors explain complex math and theory through stories, visuals, and games. You’ll learn to apply quantum to real-world applications, such as sending secret messages and solving chemistry problems. What's inside The underlying mechanics of quantum computers Simulating qubits in Python Exploring quantum algorithms with Q# Applying quantum computing to chemistry, arithmetic, and data About the reader For software developers. No prior experience with quantum computing required. About the author Dr. Sarah Kaiser works at the Unitary Fund, a non-profit organization supporting the quantum open-source ecosystem, and is an expert in building quantum tech in the lab. Dr. Christopher Granade works in the Quantum Systems group at Microsoft, and is an expert in characterizing quantum devices. Table of Contents PART 1 GETTING STARTED WITH QUANTUM 1 Introducing quantum computing 2 Qubits: The building blocks 3 Sharing secrets with quantum key distribution 4 Nonlocal games: Working with multiple qubits 5 Nonlocal games: Implementing a multi-qubit simulator 6 Teleportation and entanglement: Moving quantum data around PART 2 PROGRAMMING QUANTUM ALGORITHMS IN Q# 7 Changing the odds: An introduction to Q# 8 What is a quantum algorithm? 9 Quantum sensing: It’s not just a phase PART 3 APPLIED QUANTUM COMPUTING 10 Solving chemistry problems with quantum computers 11 Searching with quantum computers 12 Arithmetic with quantum computers
Author: Kristof T. Schütt Publisher: Springer Nature ISBN: 3030402452 Category : Science Languages : en Pages : 473
Book Description
Designing molecules and materials with desired properties is an important prerequisite for advancing technology in our modern societies. This requires both the ability to calculate accurate microscopic properties, such as energies, forces and electrostatic multipoles of specific configurations, as well as efficient sampling of potential energy surfaces to obtain corresponding macroscopic properties. Tools that can provide this are accurate first-principles calculations rooted in quantum mechanics, and statistical mechanics, respectively. Unfortunately, they come at a high computational cost that prohibits calculations for large systems and long time-scales, thus presenting a severe bottleneck both for searching the vast chemical compound space and the stupendously many dynamical configurations that a molecule can assume. To overcome this challenge, recently there have been increased efforts to accelerate quantum simulations with machine learning (ML). This emerging interdisciplinary community encompasses chemists, material scientists, physicists, mathematicians and computer scientists, joining forces to contribute to the exciting hot topic of progressing machine learning and AI for molecules and materials. The book that has emerged from a series of workshops provides a snapshot of this rapidly developing field. It contains tutorial material explaining the relevant foundations needed in chemistry, physics as well as machine learning to give an easy starting point for interested readers. In addition, a number of research papers defining the current state-of-the-art are included. The book has five parts (Fundamentals, Incorporating Prior Knowledge, Deep Learning of Atomistic Representations, Atomistic Simulations and Discovery and Design), each prefaced by editorial commentary that puts the respective parts into a broader scientific context.
Author: Bobbi DePorter
Author: Bobbi DePorter
Author: John Parks Le Tellier
Author: Bobbi DePorter
Author: Maria Schuld
Author: Conrad P. Pritscher
Author: Barbara K. Given
Author: Maria Schuld
Author: Peter Wittek
Author: Sarah C. Kaiser
Author: Kristof T. Schütt