The word "quantum" is from the Latin for "how much" and in this book mathematician David Orrell shows how it applies to the world of economic transactions. Written in clear and accessible language, the book covers the essential mathematics behind topics such as quantum cognition, option pricing, and quantum game theory, and delves into the nuts and bolts of quantum mechanics, the principles of quantum economic modelling, and the basics of quantum computation. On the way the reader will learn how quantum interference can be used to model cognitive dissonance, how a quantum walk goes further than a random walk, and how financial entanglement explains the rate of mortgage default. It is aimed at anyone who wants to understand the quantum ideas working their way into economics and finance, without getting drowned in wave equations. As interest in quantum computing grows, many companies from established banks to startups are looking at ways to perform financial simulations using quantum algorithms. But what if we should be using quantum models anyway - because the monetary system has quantum properties of its own, and because they work? The field is developing rapidly, and this second edition contains many updates including new material on quantum logic and quantum agent-based models, and a guest chapter from Ramy Aboushelbaya and Marko Mayr of Quantum Dice on quantum hardware. David Orrell is an applied mathematician with extensive experience in mathematical modelling, and the author of a dozen books on science and economics.

The word "quantum" is from the Latin for "how much" and in this book mathematician David Orrell shows how it applies to the world of economic transactions. Written in clear and accessible language, the book covers the essential mathematics behind topics such as quantum cognition, option pricing, and quantum game theory, and delves into the nuts and bolts of quantum mechanics, the principles of quantum economic modelling, and the basics of quantum computation. On the way the reader will learn how quantum interference can be used to model cognitive dissonance, how a quantum walk goes further than a random walk, and how financial entanglement explains the rate of mortgage default. It is aimed at anyone who wants to understand the quantum ideas working their way into economics and finance, without getting drowned in wave equations. As interest in quantum computing grows, many companies from established banks to startups are looking at ways to perform financial simulations using quantum algorithms. But what if we should be using quantum models anyway - because the monetary system has quantum properties of its own, and because they work? The field is developing rapidly, and this third edition contains many updates including new material on quantum logic and quantum agent-based models. David Orrell is an applied mathematician with extensive experience in mathematical modelling, and the author of a dozen books on science and economics.

A decade after the financial crisis, there is a growing consensus that economics has failed and needs to go back to the drawing board. David Orrell argues that it has been trying to solve the wrong problem all along. Economics sees itself as the science of scarcity. Instead, it should be the science of money (which plays a surprisingly small role in mainstream theory). And money is a substance that turns out to have a quantum nature of its own. Just as physicists learn about matter by studying the exchange of particles at the subatomic level, so economics should begin by analysing the nature of money-based transactions. Quantum Economics therefore starts with the meaning of the phrase 'how much' – or, to use the Latin word, quantum. From quantum physics to the dualistic properties of money, via the emerging areas of quantum finance and quantum cognition, this profoundly important book reveals that quantum economics is to neoclassical economics what quantum physics is to classical physics – a genuine turning point in our understanding.

This book provides an introduction to how the mathematical tools from quantum field theory can be applied to economics and finance. Providing a range of quantum mathematical techniques for designing financial instruments, it demonstrates how a range of topics have quantum mechanical formulations, from asset pricing to interest rates.

Given the rapid pace of development in economics and finance, a concise and up-to-date introduction to mathematical methods has become a prerequisite for all graduate students, even those not specializing in quantitative finance. This book offers an introductory text on mathematical methods for graduate students of economics and finance–and leading to the more advanced subject of quantum mathematics. The content is divided into five major sections: mathematical methods are covered in the first four sections, and can be taught in one semester. The book begins by focusing on the core subjects of linear algebra and calculus, before moving on to the more advanced topics of probability theory and stochastic calculus. Detailed derivations of the Black-Scholes and Merton equations are provided – in order to clarify the mathematical underpinnings of stochastic calculus. Each chapter of the first four sections includes a problem set, chiefly drawn from economics and finance. In turn, section five addresses quantum mathematics. The mathematical topics covered in the first four sections are sufficient for the study of quantum mathematics; Black-Scholes option theory and Merton’s theory of corporate debt are among topics analyzed using quantum mathematics.

This book applies the mathematics and concepts of quantum mechanics and quantum field theory to the modelling of interest rates and the theory of options. Particular emphasis is placed on path integrals and Hamiltonians. Financial mathematics is dominated by stochastic calculus. The present book offers a formulation that is completely independent of that approach. As such many results emerge from the ideas developed by the author. This work will be of interest to physicists and mathematicians working in the field of finance, to quantitative analysts in banks and finance firms and to practitioners in the field of fixed income securities and foreign exchange. The book can also be used as a graduate text for courses in financial physics and financial mathematics.

Machine Learning: An Applied Mathematics Introduction covers the essential mathematics behind all of the following topics - K Nearest Neighbours; K Means Clustering; Naïve Bayes Classifier; Regression Methods; Support Vector Machines; Self-Organizing Maps; Decision Trees; Neural Networks; Reinforcement Learning