Two-way and one-way quantum and classical automata with advice for online minimization problems

We consider online algorithms. Typically, the model is investigated with respect to the competitive ratio. In this paper, we explore two-way automata and one-way automata as models for online algorithms. We focus on quantum and classical online algorithms. We show that there are problems that can be solved more efficiently by two-way automata with quantum and classical states than by classical two-way automata in the case of sublogarithmic memory (sublinear size), even if classical automata get advice bits. Additionally, we show that there are problems that can be solved more efficiently by one-way quantum automata than by classical one-way automata in the case of sublogarithmic memory (resp., sublinear size) and in the case of logarithmic memory (resp., linear size) even if classical automata get advice bits.