October 2022
Intermediate to advanced
442 pages
9h 37m
English
The arrival of the new computational paradigm of quantum computing and the progress achieved in developing quantum computing hardware prompted intensive research in exploring the capabilities of quantum machine learning models and, more specifically, quantum generative models that can be viewed as quantum counterparts of the classical RBMs introduced in Chapter 5. Classical generative models form one of the most important classes of unsupervised machine learning techniques with numerous applications in finance, such as the generation of synthetic market data [48, 173], the development of systematic trading strategies [176] or data anonymisation [174], to name just a few.
Quantum generative models have all ...