Identifying the potentiality of advantage with QML
When the subject is QML, there are several potential applications in finance, as was outlined in the previous chapters. The following are a few examples:
- Risk analysis and management
- Trading
- Fraud detection
- Credit scoring
- Churn prediction
It is important to note that while there are many potential applications of quantum machine learning in finance, the field is still in its early stages. It is unclear how these algorithms will perform in practice and what the companies can expect from the QML implementations. With this in mind, analyzing how to measure the success of a quantum project can be challenging.
Despite the previously described concern, one of the interesting points about exploring quantum machine learning challenges is that popular Python SDKs such as Qiskit (IBM), TensorFlow Quantum (Google), and PennyLane (Xanadu) enable the users and quantum developers to compare “apples with apples...