Quantum Online Streaming Algorithms with Constant Number of Advice Bits
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Online algorithms are known model that is investigated with respect to a competitive ratio typically. In this paper, we investigate streaming algorithms as a model for online algorithms. We focus on quantum and classical online algorithms with advice. Special problem P_k was considered. This problem is obtained using "Black Hats Method". There is an optimal quantum online algorithm with single advice bit and single qubit of memory for the problem. At the same time, there is no optimal deterministic or randomized online algorithm with sublogarithmic memory for the same problem even the algorithm gets o( n) advice bits. Additionally, we show that quantum online algorithm with a constant number of qubits can be better than deterministic online algorithm even if deterministic one has a constant number of advice bits and unlimited computational power.
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