On a Friday afternoon in March 2021, at the height of the coronavirus pandemic, Victor Frenkel and his two teenage sons drove from their home outside Baltimore to a quiet suburb in Northern Virginia. They returned home with an eight-year-old Labrador retriever, a rescue dog that would forever change their lives. Walks with Grover is a light-hearted memoir that chronicles the year that followed. Bringing along his family and friends, the author shares some of his more humorous adventures with Grover, taking you through his home and neighborhood and in and around the Baltimore area. Ultimately, through the stories he tells, he makes the case that getting a rescue not only changes a dog's life for the better, but also that of the people who adopt them. He sincerely wishes that if you haven't done so already, you will consider contacting your local shelter or rescue organization and come home with a dog you will cherish forever--a family member who will love you unconditionally and provide companionship and happiness to you and your loved ones. Victor Frenkel was born and grew up in Montreal, Canada. At the age of twenty-four, he moved to Israel, where he lived on a kibbutz, served in the military, and completed his undergraduate and graduate studies. In 1999, he arrived in Baltimore for a postdoctoral fellowship. Today, he continues to enjoy a career in biomedical research, and occasionally dabbles in personal writing projects. He and Grover live just outside of the city with his two teenage sons. ALL PROFITS OBTAINED FROM THE SALE OF THIS BOOK WILL BE DONATED TO ANIMAL SHELTERS AND RESCUE ORGANIZATIONS

The revised edition of this book offers an extended overview of quantum walks and explains their role in building quantum algorithms, in particular search algorithms. Updated throughout, the book focuses on core topics including Grover's algorithm and the most important quantum walk models, such as the coined, continuous-time, and Szedgedy's quantum walk models. There is a new chapter describing the staggered quantum walk model. The chapter on spatial search algorithms has been rewritten to offer a more comprehensive approach and a new chapter describing the element distinctness algorithm has been added. There is a new appendix on graph theory highlighting the importance of graph theory to quantum walks. As before, the reader will benefit from the pedagogical elements of the book, which include exercises and references to deepen the reader's understanding, and guidelines for the use of computer programs to simulate the evolution of quantum walks. Review of the first edition: “The book is nicely written, the concepts are introduced naturally, and many meaningful connections between them are highlighted. The author proposes a series of exercises that help the reader get some working experience with the presented concepts, facilitating a better understanding. Each chapter ends with a discussion of further references, pointing the reader to major results on the topics presented in the respective chapter.” - Florin Manea, zbMATH.

An award-winning trainer draws on experience with such top athletes as Michael Jordan, Kobe Bryant and Ken Griffey, Jr. to explain how to tap dark competitive reflexes in order to succeed regardless of circumstances, explaining the importance of finding internal resources and harnessing the power of personal fears and instincts.

Discrete quantum walks are quantum analogues of classical random walks. They are an important tool in quantum computing and a number of algorithms can be viewed as discrete quantum walks, in particular Grover's search algorithm. These walks are constructed on an underlying graph, and so there is a relation between properties of walks and properties of the graph. This book studies the mathematical problems that arise from this connection, and the different classes of walks that arise. Written at a level suitable for graduate students in mathematics, the only prerequisites are linear algebra and basic graph theory; no prior knowledge of physics is required. The text serves as an introduction to this important and rapidly developing area for mathematicians and as a detailed reference for computer scientists and physicists working on quantum information theory.

USA TODAY Bestseller! Scarlett Goodwin’s world is divided into Before and After. Before she agreed to tutor Tucker Price, college junior Scarlett was introvert, struggling with her social anxiety and determined to not end up living in a trailer park like her mother and her younger sister. A mathematics major, she goes to her classes, to her job in the tutoring lab, and then hides in the apartment she shares with her friend, Caroline. After junior Tucker Price, Southern University’s star soccer player enters the equation, her carefully plotted life is thrown off its axis. Tucker’s failing his required College Algebra class. With his eligibility is at risk, the university chancellor dangles an expensive piece of computer software for the math department if Scarlett agrees to privately tutor him. Tucker’s bad boy, womanizer reputation makes Scarlett wary of any contact, let alone spending several hours a week in close proximity. But from her first encounter, she realizes Tucker isn’t the person everyone else sees. He carries a mountain of secrets which she suspects hold the reason to his self-destructive behavior. But the deeper she delves into the cause of his pain, the deeper she gets sucked into his chaos. Will Scarlett find the happiness she’s looking for, or will she be caught in Tucker’s aftermath?

"Combinatorial Optimization Problems: Quantum Computing" is an introductory guide that bridges the gap between combinatorial optimization and quantum computing for absolute beginners. This book unpacks fundamental concepts in optimization and explores how quantum computing can revolutionize the way we approach complex problems. Through clear explanations and relatable examples, readers will gain an understanding of both fields without needing any prior knowledge of quantum mechanics or advanced mathematics. Ideal for those curious about the future of technology, this book serves as a stepping stone into the fascinating world of quantum algorithms and their applications in optimization.

One of Springer’s renowned Major Reference Works, this awesome achievement provides a comprehensive set of solutions to important algorithmic problems for students and researchers interested in quickly locating useful information. This first edition of the reference focuses on high-impact solutions from the most recent decade, while later editions will widen the scope of the work. All entries have been written by experts, while links to Internet sites that outline their research work are provided. The entries have all been peer-reviewed. This defining reference is published both in print and on line.