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Hands-On Quantum Machine Learning with Python

Variational Hybrid Quantum Classical Algorithm

Explore how to enhance binary classification using a variational hybrid quantum-classical algorithm. Understand the process of classical data pre-processing, parameterized quantum circuit construction, and post-processing of quantum measurements to generate predictions within a closed-loop system.

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The PQC binary classifier we created in the previous lesson, Parameterized Quantum Circuit, is as good as the random classifier—or as poor, because it does not increase the information level.

This is going to change now. So far, we’ve fed the PQC with the same initial state, ∣ψ⟩=[1212]|\psi\rangle=\begin{bmatrix}\frac{1}{\sqrt{2}}\\\frac{1}{\sqrt{2}}\end{bmatrix} ...