S. Lee

Beneath the Noise: Uncovering and Distilling the Encoding Bottleneck of Quantum Models

Quantum machine learning (QML) has attracted growing interest for its potential to match large classical models with far fewer parameters. This derives from encoding classical data representations into a Hilbert space exponentially larger than the number of qubits. In practice, however, noise mitigation limits these mo...