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arXiv Machine Learning By Arka Prabha Das, Abram Magner

Advantage of Entangled Learning Rules in Quantum Measurement Class Learning

Read the original on arXiv Machine Learning →

The paper investigates quantum measurement PAC learning, where a learner selects a measurement from a hypothesis class to predict classical labels of new quantum states. It demonstrates that learning rules based on entangled measurements can outperform single‑copy (LOCC) learning rules, showing that single‑copy rules are asymptotically suboptimal in certain scenarios. The authors further prove that entangled‑measurement rules offer at most a polynomial improvement in sample complexity over single‑copy rules under a natural joint measurability assumption.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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