A new arXiv preprint explores the challenges of stabilizer state testing and learning within limited quantum memory. Researchers found that when quantum memory is restricted to $k$ qubits out of $n$, the distinction between testing and learning stabilizer states collapses. Specifically, testing requires $O(n-k)$ copies, and learning requires $O(n^2/k)$ copies in a non-adaptive framework, indicating that limited memory makes testing as difficult as learning. AI
RANK_REASON The cluster discusses a new academic paper detailing theoretical findings in quantum computing. [lever_c_demoted from research: ic=2 ai=0.4]
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