2024
Lifting query complexity to time-space complexity for two-way finite automata
ZHENG, Shenggen; Yaqiao LI; Minghua PAN; Jozef GRUSKA; Lvzhou LI et. al.Základní údaje
Originální název
Lifting query complexity to time-space complexity for two-way finite automata
Autoři
ZHENG, Shenggen (156 Čína); Yaqiao LI; Minghua PAN; Jozef GRUSKA (703 Slovensko, domácí) a Lvzhou LI
Vydání
Journal of Computer and System Sciences, San Diego, Elsevier, 2024, 0022-0000
Další údaje
Jazyk
angličtina
Typ výsledku
Článek v odborném periodiku
Obor
10201 Computer sciences, information science, bioinformatics
Stát vydavatele
Spojené státy
Utajení
není předmětem státního či obchodního tajemství
Odkazy
Impakt faktor
Impact factor: 0.900
Kód RIV
RIV/00216224:14330/24:00139267
Organizační jednotka
Fakulta informatiky
UT WoS
001138384300001
EID Scopus
2-s2.0-85179893976
Klíčová slova anglicky
Quantum computing; Time-space complexity; Two-way finite automata; Communication complexity; Lifting theorems; Query algorithms
Příznaky
Mezinárodní význam, Recenzováno
Změněno: 2. 4. 2025 00:48, RNDr. Pavel Šmerk, Ph.D.
Anotace
V originále
Time-space tradeoff has been studied in a variety of models, such as Turing machines, branching programs, and finite automata, etc. While communication complexity as a technique has been applied to study finite automata, it seems it has not been used to study time-space tradeoffs of finite automata. We design a new technique showing that separations of query complexity can be lifted, via communication complexity, to separations of time-space complexity of two-way finite automata. As an application, one of our main results exhibits the first example of a language L such that the time-space complexity of two-way probabilistic finite automata with a bounded error (2PFA) is ⠂⠂(n2), while of exact two-way quantum finite automata with classical states (2QCFA) is O ⠂(n5/3), that is, we demonstrate for the first time that exact quantum computing has an advantage in time-space complexity comparing to classical computing. (c) 2023 Elsevier Inc. All rights reserved.