Quantum versus Classical Online Streaming Algorithms with Advice
Abstract
We consider online algorithms with respect to the competitive ratio. Here, we investigate quantum and classical one-way automata with non-constant size of memory (streaming algorithms) as a model for online algorithms. We construct problems that can be solved by quantum online streaming algorithms better than by classical ones in a case of logarithmic or sublogarithmic size of memory, even if classical online algorithms get advice bits. Furthermore, we show that a quantum online algorithm with a constant number of qubits can be better than any deterministic online algorithm with a constant number of advice bits and unlimited computational power.
- Publication:
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arXiv e-prints
- Pub Date:
- February 2018
- DOI:
- 10.48550/arXiv.1802.05134
- arXiv:
- arXiv:1802.05134
- Bibcode:
- 2018arXiv180205134K
- Keywords:
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- Computer Science - Data Structures and Algorithms;
- Computer Science - Computational Complexity;
- Quantum Physics
- E-Print:
- arXiv admin note: substantial text overlap with arXiv:1710.09595