Generative Quantum Data Embeddings for Supervised Learning
Many practically relevant applications of quantum machine learning involve classical data, for which performance depends critically on how inputs are embedded into quantum states. Yet the use of a fixed embedding circuit ansatz remains standard practice. We propose an energy-based generative learning framework that synthesizes gate sequences to optimize embedding structures and refine data-tailored parameters, using a fidelity-based surrogate objective to guide the search toward improved class distinguishability. Empirically, the method improves classification performance across diverse settings, while also revealing datasets where architecture search within the present embedding family y...
| Authors | Jaewoong Heo, Daniel K. Park |
|---|---|
| Primary category | quant-ph |
| Published | 29 May 2026 |
| Last updated at source | 29 May 2026 |
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