We present an explicit embedding of measure-once quantum finite automata (MO-QFAs) into the Feynman quantum computer model.Given an MO-QFA, we construct the corresponding Feynman machine whose register encodes both the input word and the automaton state, while a cursor evolving under a Hamiltonian implements the sequential application of the automaton transition operators.The construction relies on nested SWITCH mechanisms that select transitions according to the input symbols.We prove that, for every MO-QFA and every input word, measuring the cursor in its final position yields exactly the quantum state reached by the automaton after processing the same input.Consequently, acceptance probabilities are preserved exactly, and every language recognized by an MO-QFA with cutpoint can be recognized within the Feynman framework with the same acceptance behavior.This establishes an explicit connection between discrete-time quantum computation and continuous-time, physically motivated Hamiltonian models.
We present an explicit embedding of measure-once quantum finite automata (MO-QFAs) into the Feynman quantum computer model.Given an MO-QFA, we construct the corresponding Feynman machine whose register encodes both the input word and the automaton state, while a cursor evolving under a Hamiltonian implements the sequential application of the automaton transition operators.The construction relies on nested SWITCH mechanisms that select transitions according to the input symbols.We prove that, for every MO-QFA and every input word, measuring the cursor in its final position yields exactly the quantum state reached by the automaton after processing the same input.Consequently, acceptance probabilities are preserved exactly, and every language recognized by an MO-QFA with cutpoint can be recognized within the Feynman framework with the same acceptance behavior.This establishes an explicit connection between discrete-time quantum computation and continuous-time, physically motivated Hamiltonian models.
The 2-way quantum finite automaton introduced by Kondacs and Watrous [Proceedings of the 38th Annual Symposium on Foundations of Computer Science, 1997, IEEE Computer Society, pp. 66--75] can accept nonregular languages with bounded error in polynomial time. If we restrict the head of the automaton to moving classically and to moving only in one direction, the acceptance power of this 1-way quantum finite automaton is reduced to a proper subset of the regular languages. In this paper we study two different models of 1-way quantum finite automata. The first model, termed measure-once quantum finite automata, was introduced by Moore and Crutchfield [Theoret. Comput. Sci., 237 (2000), pp. 275--306], and the second model, termed measure-many quantum finite automata, was introduced by Kondacs and Watrous [Proceedings of the38th Annual Symposium on Foundations of Computer Science, 1997, IEEE Computer Society, pp. 66--75]. We characterize the measure-once model when it is restricted to accepting with bounded error and show that, without that restriction, it can solve the word problem over the free group. We also show that it can be simulated by a probabilistic finite automaton and describe an algorithm that determines if two measure-once automata are equivalent. We prove several closure properties of the classes of languages accepted by measure-many automata, including inverse homomorphisms, and provide a new necessary condition for a language to be accepted by the measure-many model with bounded error. Finally, we show that piecewise testable sets can be accepted with bounded error by a measure-many quantum finite automaton, introducing new construction techniques for quantum automata in the process.
A quantum finite-state automaton (QFA) is a theoretical model designed to simulate the evolution of a quantum system with finite memory in response to sequential input strings. We define the language of a QFA as the set of strings that lead the QFA to an accepting state when processed from its initial state. QFAs exemplify how quantum computing can achieve greater efficiency compared to classical computing. While being one of the simplest quantum models, QFAs are still notably challenging to construct from scratch due to the preliminary knowledge of quantum mechanics required for superimposing unitary constraints on the automata. Furthermore, even when QFAs are correctly assembled, the limitations of a current quantum computer may cause fluctuations in the simulation results depending on how an assembled QFA is translated into a quantum circuit. We present a framework that provides a simple and intuitive way to build QFAs and maximize the simulation accuracy. Our framework relies on two methods: First, it offers a predefined construction for foundational types of QFAs that recognize special languages MOD and EQU. They play a role of basic building blocks for more complex QFAs. In other words, one can obtain more complex QFAs from these foundational automata using standard language operations. Second, we improve the simulation accuracy by converting these QFAs into quantum circuits such that the resulting circuits perform well on noisy quantum computers. Our framework is available at https://github.com/sybaik1/qfa-toolkit.
In the contemporary era we dwell in, every now and then, new technologies are developed by master brains such as researchers and scientists. This also paves the way to discover replacements to classical computers. One such progression among the great inventions is the essence of quantum computing. They are brought to light for the purpose of solving problems and scenarios solved by classical computers but in a more effective way and in the least span of time. The study of quantum computing focuses on finding new ways to compute by using quantum physics phenomena. A qubit, in contrast to a typical computer bit, can be either 0 or 1, or it can be a superposition of both 0 and 1. Quantum theory commemorates with automata theory, thereby formulating quantum finite automata. Quantum finite automaton, or basically a deterministic finite automaton, is one of the simplest models of computation, which is an input-driven model, which takes an input, and according to the input, we just read the machine, and it changes its state. This proves that any quantum finite automata are completely capable of recognizing languages like Tally with given cut point too. Such languages recognized by a cut point, by quantum finite automata, are called stochastic languages. It further collaborates with the chemical industry with its qubit power over molecular collide, interactive models, and collaborative string processing using nonregular languages.
We consider notions of freeness and ambiguity for the acceptance probability of Moore-Crutchfield Measure Once Quantum Finite Automata (MO-QFA). We study the distribution of acceptance probabilities of such MO-QFA, which is partly motivated by similar freeness problems for matrix semigroups and other computational models. We show that determining if the acceptance probabilities of all possible input words are unique is undecidable for 32 state MO-QFA, even when all unitary matrices and the projection matrix are rational and the initial configuration is defined over real algebraic numbers. We utilize properties of the skew field of quaternions, free rotation groups, representations of tuples of rationals as a linear sum of radicals and a reduction of the mixed modification Post’s correspondence problem.
Abstract Learning finite automata (termed as model learning) has become an important field in machine learning and has been useful realistic applications. Quantum finite automata (QFA) are simple models of quantum computers with finite memory. Due to their simplicity, QFA have well physical realizability, but one-way QFA still have essential advantages over classical finite automata with regard to state complexity (two-way QFA are more powerful than classical finite automata in computation ability as well). As a different problem in quantum learning theory and quantum machine learning, in this paper, our purpose is to initiate the study of learning QFA with queries (naturally it may be termed as quantum model learning), and the main results are regarding learning two basic one-way QFA (1QFA): (1) we propose a learning algorithm for measure-once 1QFA (MO-1QFA) with query complexity of polynomial time and (2) we propose a learning algorithm for measure-many 1QFA (MM-1QFA) with query complexity of polynomial time, as well.
In the past year two different models of quantum finite automata have been proposed. The first model, introduced by Moore and Crutchfield[MC98], makes one measurement on its state at the end of its computation and is called a measure-once quantum finite automata. The second model, introduced by Kondacs and Watrous[KW97], makes a measurement of its state after every transition and is called a measure-many quantum finite automata. In this thesis we investigate the characteristics of the two models. We characterize measure-once quantum finite automata when they are restricted to acceptance with bounded error, and we show that, when they are not so restricted, they can solve the word problem over the free group. We show that they can be simulated by probabilistic finite automata, which implies that they are no more powerful than probabilistic finite automata. We also describe an algorithm that determines if two automata are equivalent. We show that the class of languages accepted by measure-many quantum finite automata is closed under quotient, complement, and inverse homomorphisms. We prove a necessary condition for a language to be accepted by a measure-many automaton with bounded error and we show that certain sets, including piecewise testable sets, can be accepted with bounded error by this automata.