Quantum computing
As we saw in the previous section, in order to estimate the future price of an equity, many iterations over potential future prices need to be run. However, what if we could establish a way to load those potential distributions into quantum states and evaluate them using quantum devices? The following subsection will dig into different ways to load those future price distributions into quantum states using existing solutions for direct loading, such as Qiskit functionalities and adversarial training using PennyLane, which might be better suited for ML tasks due to its differentiable programming approach (similar to TensorFlow or PyTorch in the classical ML domain).
Implementation in Qiskit
As discussed in Chapter 2, Qiskit is one of the most mature quantum computing frameworks available and counts with higher-level modules so that specific applications can be easily translated to the quantum regime. This is the case with Qiskit Finance, which we will explore...
