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Quantum Machine Learning and Optimisation in Finance. Drive financial innovation with quantum-powered algorithms and optimisation strategies - Second Edition Antoine Jacquier, Oleksiy Kondratyev, Alexander Lipton, Marcos López de Prado

Język publikacji: angielskim
Quantum Machine Learning and Optimisation in Finance. Drive financial innovation with quantum-powered algorithms and optimisation strategies - Second Edition Antoine Jacquier, Oleksiy Kondratyev, Alexander Lipton, Marcos López de Prado - okladka książki

Quantum Machine Learning and Optimisation in Finance. Drive financial innovation with quantum-powered algorithms and optimisation strategies - Second Edition Antoine Jacquier, Oleksiy Kondratyev, Alexander Lipton, Marcos López de Prado - okladka książki

Autorzy:
Antoine Jacquier, Oleksiy Kondratyev, Alexander Lipton, Marcos López de Prado
Serie wydawnicze:
Hands-on
Ocena:
Stron:
494
Zostało Ci na świąteczne zamówienie opcje wysyłki »
As quantum machine learning (QML) continues to evolve, many professionals struggle to apply its powerful algorithms to real-world problems using noisy intermediate-scale quantum (NISQ) hardware. This book bridges that gap by focusing on hands-on QML applications tailored to NISQ systems, moving beyond the traditional textbook approaches that explore standard algorithms like Shor's and Grover's, which lie beyond current NISQ capabilities.
You’ll get to grips with major QML algorithms that have been widely studied for their transformative potential in finance and learn hybrid quantum-classical computational protocols, the most effective way to leverage quantum and classical computing systems together.
The authors, Antoine Jacquier, a distinguished researcher in quantum computing and stochastic analysis, and Oleksiy Kondratyev, a Quant of the Year awardee with over 20 years in quantitative finance, offer a hardware-agnostic perspective. They present a balanced view of both analog and digital quantum computers, delving into the fundamental characteristics of the algorithms while highlighting the practical limitations of today’s quantum hardware.
By the end of this quantum book, you’ll have a deeper understanding of the significance of quantum computing in finance and the skills needed to apply QML to solve complex challenges, driving innovation in your work.

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O autorach książki

Antoine Jacquier obtained his PhD in 2010 in Mathematics from Imperial College London, where his research was focused on large deviations and asymptotic methods for stochastic volatility. Over the past 10 years, he has been working on stochastic analysis and volatility modelling, publishing about 50 papers and co-writing several books. He is also the Head of the MSc in Mathematics and Finance at Imperial College and regularly works as a quantitative consultant for the Finance industry.
Oleksiy Kondratyev obtained his PhD in Mathematical Physics from the Institute for Mathematics, National Academy of Sciences of Ukraine, where his research was focused on studying phase transitions in quantum lattice systems. Oleksiy has over 20 years of quantitative finance experience, primarily in banking. He was recognised as Quant of the Year 2019 by Risk magazine and joined Abu Dhabi Investment Authority as a Quantitative Research & Development Lead in the summer of 2021.

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