研究人员探讨了在相干量子内存有限的情况下，量子计算中稳定器状态测试与学习的复杂性。他们发现，在内存限制下，测试和学习这些状态的难易程度之间的分离（在不受限制的内存下存在）消失了。具体来说，具有 $k$ 个量子比特内存的稳定器状态的测试样本复杂度随 $n-k$ 缩放，而非自适应学习则需要 $\Theta(n^2/k)$ 份副本。这项工作强调了相干量子内存是实现稳定器测试与学习之间典型区别的关键资源。 AI
影响 识别量子态操纵中的关键资源限制，可能影响未来的量子算法设计。
排序理由 阐述量子计算理论发现的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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arXiv:2607.02444v1 Announce Type: cross Abstract: We study stabilizer state testing and learning with limited coherent quantum memory. Here an algorithm sequentially receives copies of an unknown $n$-qubit state, but may keep only $k$ qubits of coherent quantum memory between mea…
We study stabilizer state testing and learning with limited coherent quantum memory. Here an algorithm sequentially receives copies of an unknown $n$-qubit state, but may keep only $k$ qubits of coherent quantum memory between measurements. With unrestricted memory, seminal work …