State complexity of one-way quantum finite automata together with classical states
Ligang Xiao, Daowen Qiu

TL;DR
This paper investigates the state complexity of one-way quantum finite automata with classical states (1QFAC), providing bounds, optimizations, and comparisons to classical automata, highlighting their efficiency and relationships with other quantum models.
Contribution
It refines bounds on the relationship between quantum and classical states in 1QFAC, and demonstrates their exponential succinctness over classical automata for certain languages.
Findings
Optimized the bound relating quantum and classical states in 1QFAC.
Provided an upper bound on classical states when quantum basis states are reduced.
Established a lower bound on classical states for recognizing regular languages.
Abstract
One-way quantum finite automata together with classical states (1QFAC) proposed in [Journal of Computer and System Sciences 81(2) (2015) 359--375] is a new one-way quantum finite automata (1QFA) model that integrates quantum finite automata (QFA) and deterministic finite automata (DFA). This model uses classical states to control the evolution and measurement of quantum states. As a quantum-classical hybrid model, 1QFAC recognize all regular languages. It was shown that the state complexity of 1QFAC for some languages is essentially superior to that of DFA and other 1QFA. In this paper, our goal is to clarify state complexity problems for 1QFAC. We obtain the following results: (1) We optimize the bound given by Qiu et al. that characterizes the relationship between quantum basis state number and classical state number of 1QFAC as well as the state number of its corresponding minimal…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
Topicssemigroups and automata theory · Machine Learning and Algorithms · DNA and Biological Computing
