Quantum versus Classical Online Algorithms with Advice and Logarithmic Space
AI Breakdown
Get a structured breakdown of this paper — what it's about, the core idea, and key takeaways for the field.
Abstract
In this paper, we consider online algorithms. Typically the model is investigated with respect to competitive ratio. We consider algorithms with restricted memory (space) and explore their power. We focus on quantum and classical online algorithms. We show that there are problems that can be better solved by quantum algorithms than classical ones in a case of logarithmic memory. Additionally, we show that quantum algorithm has an advantage, even if deterministic algorithm gets advice bits. We propose "Black Hats Method". This method allows us to construct problems that can be effectively solved by quantum algorithms. At the same time, these problems are hard for classical algorithms. The separation between probabilistic and deterministic algorithms can be shown with a similar method.