Researchers have explored the complexities of testing and learning stabilizer states in quantum computing when limited coherent quantum memory is available. They found that the separation between the ease of testing and the difficulty of learning these states, which exists with unrestricted memory, disappears under memory constraints. Specifically, the sample complexity for testing stabilizer states with $k$ qubits of memory scales with $n-k$, while learning them non-adaptively requires $\Theta(n^2/k)$ copies. This work highlights coherent quantum memory as the key resource enabling the typical distinction between stabilizer testing and learning. AI
IMPACT Identifies key resource constraints in quantum state manipulation, potentially influencing future quantum algorithm design.
RANK_REASON Academic paper detailing theoretical findings in quantum computing. [lever_c_demoted from research: ic=1 ai=1.0]
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