Cloning is as Hard as Learning for Stabilizer States
The impossibility of simultaneously cloning non-orthogonal states lies at the foundations of quantum theory. Even when allowing for approximation errors, cloning an arbitrary unknown pure state requires as many initial copies as needed to fully learn the state. Rather than arbitrary unknown states, modern quantum learning theory often considers structured classes of states and exploits such structure to develop learning algorithms that outperform general-state tomography. This raises the question: How do the sample complexities of learning and cloning relate for such structured classes? We answer this question for an important class of states. Namely, for $n$-qubit stabilizer states, we s...
| Authors | Nikhil Bansal, Matthias C. Caro, Gaurav Mahajan |
|---|---|
| Primary category | quant-ph |
| Published | 16 April 2026 |
| Last updated at source | 16 April 2026 |
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