Exponentially more concise quantum recognition of non-RMM regular languages
Daowen Qiu, Lvzhou Li, Paulo Mateus, Amilcar Sernadas

TL;DR
This paper introduces a new quantum automaton model, 1QFAC, which can accept all regular languages with exponential state efficiency over classical DFA, and explores its properties and decision problems.
Contribution
The paper presents 1QFAC, a novel quantum automaton model that recognizes all regular languages with exponential state savings and analyzes its equivalence and minimization.
Findings
1QFAC accepts all regular languages with bounded error.
1QFAC can be exponentially more concise than DFA.
State minimization of 1QFAC is decidable within EXPSPACE.
Abstract
We show that there are quantum devices that accept all regular languages and that are exponentially more concise than deterministic finite automata (DFA). For this purpose, we introduce a new computing model of {\it one-way quantum finite automata} (1QFA), namely, {\it one-way quantum finite automata together with classical states} (1QFAC), which extends naturally both measure-only 1QFA and DFA and whose state complexity is upper-bounded by both. The original contributions of the paper are the following. First, we show that the set of languages accepted by 1QFAC with bounded error consists precisely of all regular languages. Second, we prove that 1QFAC are at most exponentially more concise than DFA. Third, we show that the previous bound is tight for families of regular languages that are not recognized by measure-once (RMO), measure-many (RMM) and multi-letter 1QFA. % More concretely…
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Taxonomy
TopicsQuantum Computing Algorithms and Architecture · Machine Learning and Algorithms · Computability, Logic, AI Algorithms
