Towards advanced data encoding techniques for quantum re-uploading models

A new interdisciplinary research topic that goes by the name of Quantum Machine Learning (QML) has recently begun to explore the interplay of ideas from quantum computing and machine learning. For example, QML investigates whether quantum computers can speed up the time it takes to train or evaluate a machine learning model. On the other hand, the QML community leverages techniques from machine learning to devise new quantum error-correcting codes, estimate the properties of quantum systems, or develop new quantum algorithms. The state of the art of Quantum Machine Learning has as main highlight the development of algorithms that have been proven to have a quantum advantage in computational complexity over classical algorithms. However, these algorithms require a fault-tolerant quantum computer, which is able to continuously correct errors that arise during computation, ensuring the stability and reliability of the quantum processes over extended periods. As we are still many years away from fault tolerant quantum computation, the QML community has developed a great interest toward possible applications of current and near-term quantum devices (NISQ devices), which are not capable of continuous quantum error correction. In particular, the QML research community has developed a new class of quantum procedures called variational quantum algorithms (VQA) to take advantage of current and near-term quantum hardware (figure 1).