Further reading
A quintessential part of what we have discussed in this chapter relates to some of the foundational algorithms in quantum computing. Grover’s algorithm (Jozsa 1999) and QAE (Rao et al. 2020) are not only key contenders for financial use cases but also for numerous applications pertaining to quantum algorithms.
More and more, QML is gaining relevance, as it allows the exploitation of existing data to create those embeddings or dynamics that quantum algorithms often require. Chapter 6 will examine in more detail these techniques. However, for those already knowledgeable about classical generative models such as GANs, variational autoencoders, and neural networks in general, there is plenty of literature that can be found to help their adaptation to the Quantum regime (Lloyd and Weedbrook, 2018). New ways that QNNs can be exploited for financial applications (Tapia et al. 2022) or different perspectives on how a price projection can be tackled constantly appear...
