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Finite-State Machines (FSMs) are critical for modeling the operational logic of network protocols, enabling verification, analysis, and vulnerability discovery. However, existing FSM extraction techniques face limitations such as…

Computation and Language · Computer Science 2025-07-16 Fares Wael , Youssef Maklad , Ali Hamdi , Wael Elsersy

Quantum states obey an asymptotic no-cloning theorem, stating that no deterministic machine can reliably replicate generic sequences of identically prepared pure states. In stark contrast, we show that generic sequences of unitary gates can…

Quantum Physics · Physics 2015-04-01 G. Chiribella , Y. Yang , C. Huang

This paper discusses "computational" systems capable of "computing" functions not computable by predefined Turing machines if the systems are not isolated from their environment. Roughly speaking, these systems can change their finite…

Artificial Intelligence · Computer Science 2009-08-03 Kurt Ammon

We give a simple Sch\"onhage Storage Modification Machine that simulates one iteration of the Rule 110 cellular automaton. This provides an alternative construction to Sch\"onhage's original proof of the Turing completeness of the eponymous…

Artificial Intelligence · Computer Science 2021-08-20 J. -M. Chauvet

Reservoir computers (RCs) and recurrent neural networks (RNNs) can mimic any finite-state automaton in theory, and some workers demonstrated that this can hold in practice. We test the capability of generalized linear models, RCs, and Long…

Machine Learning · Computer Science 2019-10-18 S. E. Marzen , J. P. Crutchfield

Quantum measurement is universal for quantum computation. This universality allows alternative schemes to the traditional three-step organisation of quantum computation: initial state preparation, unitary transformation, measurement. In…

Quantum Physics · Physics 2007-05-23 Simon Perdrix , Philippe Jorrand

We present the Random Permutation Sorting System (RPSS), a novel framework for true uniform randomness generation grounded in statistical quantum mechanics. RPSS is built on a pair of conjugate observables, the permutation count and the…

Quantum Physics · Physics 2026-02-10 Randy Kuang

This paper introduces a new computing model based on the cooperation among Turing machines called orchestrated machines. Like universal Turing machines, orchestrated machines are also designed to simulate Turing machines but they can also…

Artificial Intelligence · Computer Science 2015-03-13 Norbert Bátfai

While prior work established a verifier-based polynomial-time framework for NP, explicit deterministic machines for concrete NP-complete problems have remained elusive. In this paper, we construct fully specified deterministic Turing…

Computational Complexity · Computer Science 2026-04-30 Changryeol Lee

The recently introduced Thermodynamic Binding Networks (TBN) model was developed with the purpose of studying self-assembling systems by focusing on their thermodynamically favorable final states, and ignoring the kinetic pathways through…

Emerging Technologies · Computer Science 2018-02-09 Cameron Chalk , Jacob Hendricks , Matthew J. Patitz , Michael Sharp

Foundations of the theory of quantum Turing machines are investigated. The protocol for the preparation and the measurement of quantum Turing machines is discussed. The local transition functions are characterized for fully general quantum…

Quantum Physics · Physics 2007-05-23 Masanao Ozawa

We propose a self-organizing memory architecture for perceptual experience, capable of supporting autonomous learning and goal-directed problem solving in the absence of any prior information about the agent's environment. The architecture…

Artificial Intelligence · Computer Science 2015-02-24 Dan P. Guralnik , Daniel E. Koditschek

In this study we employ a feed-forward artificial neural network (FFNN) architecture to perform tomography of quantum states and processes obtained from noisy experimental data. To evaluate the performance of the FFNN, we use a heavily…

Quantum Physics · Physics 2024-11-05 Akshay Gaikwad , Omkar Bihani , Arvind , Kavita Dorai

Machine Unlearning has emerged as a significant area of research, focusing on `removing' specific subsets of data from a trained model. Fine-tuning (FT) methods have become one of the fundamental approaches for approximating unlearning, as…

Machine Learning · Computer Science 2025-11-25 Meng Ding , Rohan Sharma , Changyou Chen , Jinhui Xu , Kaiyi Ji

An introductory formal languages course exposes advanced undergraduate and early graduate students to automata theory, grammars, constructive proofs, computability, and decidability. Programming students find these topics to be challenging…

Formal Languages and Automata Theory · Computer Science 2014-12-17 Marco T. Morazán , Rosario Antunez

Harnessing the intrinsic dynamics of physical systems for information processing opens new avenues for computation embodied in matter. Using simulations of a model system, we show that assemblies of DNA tiles capable of self-organizing into…

Soft Condensed Matter · Physics 2025-10-23 Tim E. Veenstra , René van Roij , Marjolein Dijkstra

It is well known that canonical recurrent neural networks (RNNs) face limitations in learning long-term dependencies which have been addressed by memory structures in long short-term memory (LSTM) networks. Neural Turing machines (NTMs) are…

Machine Learning · Computer Science 2023-10-06 Animesh Renanse , Alok Sharma , Rohitash Chandra

In this note we give a simple unifying proof of the undecidability of several diagrammatic properties of term rewriting systems that include: local confluence, strong confluence, diamond property, subcommutative property, and the existence…

Logic in Computer Science · Computer Science 2019-10-22 António Malheiro , Paulo Guilherme Santos

This paper goes back to Turing (1936) and treats his machine as a cognitive model (W,D,B), where W is an "external world" represented by memory device (the tape divided into squares), and (D,B) is a simple robot that consists of the…

Artificial Intelligence · Computer Science 2007-05-23 Victor Eliashberg

Matrix multiplication (GEMM) is a core operation to numerous scientific applications. Traditional implementations of Strassen-like fast matrix multiplication (FMM) algorithms often do not perform well except for very large matrix sizes, due…

Mathematical Software · Computer Science 2016-11-04 Jianyu Huang , Leslie Rice , Devin A. Matthews , Robert A. van de Geijn