Quantum Aliasing - Quantum Machine Language

What is it about?

Quantum machine Learning (QML) is an emerging technique that leverages quantum theory and the yet-to-be fully developed quantum computers. QML may help solve classification problems that cannot be resolved by deep neural network (DNN). However, QML suffers from a quantum aliasing problem that is created by the inevitable downsampling and binarization operations. QML takes classical data domain as its input, transforms it into a domain of quantum states (a subset of the Hilbert space) using quantum encoding, generates quantum feature vectors, and builds a quantum circuit on the quantum feature vectors—as a machine learning model—to perform classification tasks. Hence, quantum encoding is an important task in QML.

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Photo by Michael Dziedzic on Unsplash

Photo by Michael Dziedzic on Unsplash