考虑空间复杂度的量子在线算法
Quantum Online Algorithms with Respect to Space Complexity
摘要：
在线算法是一种著名的计算模型。我们引入量子在线算法，并从两个角度研究它们的竞争比：空间复杂度和建议复杂度。我们从探索一个模型与有限的内存和量子在线算法可以优于经典的（确定性或随机）的亚对数空间（内存），他们可以优于确定性在线算法没有限制的内存。此外，我们考虑了多对数空间的情况，并表明在这种情况下，量子在线算法也可以比确定性算法更好。
英文摘要：
Online algorithm is a well-known computational model. We introduce quantum online algorithms and investigate them with respect to a competitive ratio in two points of view: space complexity and advice complexity. We start with exploring a model with restricted memory and show that quantum online algorithms can be better than classical ones (deterministic or randomized) for sublogarithmic space (memory), and they can be better than deterministic online algorithms without restriction for memory. Additionally, we consider polylogarithmic space case and show that in this case, quantum online algorithms can be better than deterministic ones as well.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
