A Tutorial on Quantum Amplitude Embedding with Data Re-Uploading
Amplitude embedding lets a variational quantum model read many classical features with few qubits, but the model output is then only a quadratic form in the normalized input. Data re-uploading, which encodes the input repeatedly between trainable blocks, can be used to enrich such a model. Because every additional upload of an amplitude embedding repeats a costly state preparation, the gain that an upload buys has to be known before the gates are spent. This tutorial thus addresses the problem of explaining what data re-uploading adds to an amplitude-embedded model and how large the gain is. We express sequential re-uploading and parallel re-uploading with two copies in one notation and s...
| Authors | Hyojun Ahn, Joongheon Kim |
|---|---|
| Primary category | quant-ph |
| Published | 13 October 2026 |
| Last updated at source | 13 October 2026 |
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