A Tutorial on Quantum Amplitude Embedding with Data Re-Uploading

Key points

• Data re-uploading, which encodes the input repeatedly between trainable blocks, can be used to enrich such a model.1
• 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.1
• This tutorial thus addresses the problem of explaining what data re-uploading adds to an amplitude-embedded model and how large the gain is.1
• We express sequential re-uploading and parallel re-uploading with two copies in one notation and show that each upload raises the reachable polynomial order by two.1

Sources (1)

• [1]A Tutorial on Quantum Amplitude Embedding with Data Re-Uploading
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 13, 06:56 AM

  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.

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