Vol. 7, Issue 2, Part C (2025)

We investigate two fundamental data encoding strategies for gate-based quantum computers: amplitude encoding and angle encoding. In amplitude encoding, a normalized  -dimensional classical vector is mapped into the probability amplitudes of a  -qubit quantum state, whereas angle encoding uses one or more single-qubit rotation gates per feature to encode data as angles.^{[1, 2]} We construct a toy binary classification dataset with four real-valued features and compare these encoding schemes using a variational quantum classifier (VQC) approach. Our results indicate that angle encoding often yields slightly higher classification accuracy (e.g. 82% vs. 75% in one experiment) at the cost of requiring more qubits, while amplitude encoding compresses features into fewer qubits (2 vs. 4) but demands deeper state-preparation circuits. These findings align with previous studies that report higher accuracy for rotation encodings (e.g.   vs.   in MNIST experiments ^[3]) and note that the encoding choice acts as a hyperparameter affecting VQC performance. ^[4]