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We investigate a variant of the fuel-based approach to modeling diverging computation in type theories and use it to abstractly capture the essence of oracle Turing machines. The resulting objects we call continuous machines. We prove that…

Logic in Computer Science · Computer Science 2020-05-05 Michal Konečný , Florian Steinberg , Holger Thies

Recent technological developments have focused the interest of the quantum computing community on investigating how near-term devices could outperform classical computers for practical applications. A central question that remains open is…

Quantum Physics · Physics 2021-11-24 Daniel Stilck Franca , Raul Garcia-Patron

Optimization problems are a staple of today's scientific and technical landscape. However, at present, solvers of such problems are almost exclusively run on digital hardware. Using Turing machines as a mathematical model for any type of…

Optimization and Control · Mathematics 2023-01-18 Yunseok Lee , Holger Boche , Gitta Kutyniok

Recently, increased computational power and data availability, as well as algorithmic advances, have led machine learning techniques to impressive results in regression, classification, data-generation and reinforcement learning tasks.…

Harvey Friedman gives a comparatively short description of an ``unimaginably large'' number $n(3)$ , beyond, e.g. the values $$ A(7,184)< A({7198},158386) < n(3)$$ of Ackermann's function - but finite. We implement Friedman's combinatorial…

Combinatorics · Mathematics 2023-03-07 Michael Vielhaber , Mónica del Pilar Canales Chacón , Sergio Jara Ceballos

There is one, and only one way, consistent with fundamental physics, that the efficiency of general digital computation can continue increasing indefinitely, and that is to apply the principles of reversible computing. We need to begin…

Emerging Technologies · Computer Science 2018-03-09 Michael P. Frank

Metastability is a spurious mode of operation in digital signals, where an electrical signal fails to settle into a stable state within a specified time, leading to uncertainty and potentially failing downstream hardware. A system that…

Computational Complexity · Computer Science 2026-04-21 Johannes Bund , Amir Leshem , Moti Medina

We prove that any Turing machine running on inputs of arbitrary length can be simulated by a constant bit-size transformer, as long as the context window is sufficiently long. This improves previous works, which require scaling up either…

Computational Complexity · Computer Science 2025-09-30 Qian Li , Yuyi Wang

Bifurcated supertasks entail the actual infinite division of time (accelerated system of reference) as well as the existence of half-curves of infinite length (supertask system of reference). This paper analyzes both issues from a critique…

General Mathematics · Mathematics 2012-01-26 Antonio Leon

These lecture notes highlight the mathematical and computational structure relating to the formulation of, and development of algorithms for, the Bayesian approach to inverse problems in differential equations. This approach is fundamental…

Probability · Mathematics 2015-07-03 Masoumeh Dashti , Andrew M. Stuart

We introduce a new concept of approximation applicable to decision problems and functions, inspired by Bayesian probability. From the perspective of a Bayesian reasoner with limited computational resources, the answer to a problem that…

Computational Complexity · Computer Science 2025-06-27 Vanessa Kosoy , Alexander Appel

To date, work on formalizing connectionist computation in a way that is at least Turing-complete has focused on recurrent architectures and developed equivalences to Turing machines or similar super-Turing models, which are of more…

Artificial Intelligence · Computer Science 2015-05-04 Anthony Di Franco

Traditional computers work with finite numbers. Situations where the usage of infinite or infinitesimal quantities is required are studied mainly theoretically. In this paper, a recently introduced computational methodology (that is not…

Numerical Analysis · Mathematics 2012-03-15 Yaroslav D. Sergeyev

Quantum computation with quantum data that can traverse closed timelike curves represents a new physical model of computation. We argue that a model of quantum computation in the presence of closed timelike curves can be formulated which…

Quantum Physics · Physics 2008-11-26 Dave Bacon

Some approaches to solving challenging dynamic programming problems, such as Q-learning, begin by transforming the Bellman equation into an alternative functional equation, in order to open up a new line of attack. Our paper studies this…

Optimization and Control · Mathematics 2019-12-05 Qingyin Ma , John Stachurski

We consider a natural scheduling problem which arises in many distributed computing frameworks. Jobs with diverse resource requirements (e.g. memory requirements) arrive over time and must be served by a cluster of servers, each with a…

Networking and Internet Architecture · Computer Science 2019-01-21 Konstantinos Psychas , Javad Ghaderi

There are several forms of irreducibility in computing systems, ranging from undecidability to intractability to nonlinearity. This paper is an exploration of the conceptual issues that have arisen in the course of investigating speed-up…

Computational Complexity · Computer Science 2011-06-24 Hector Zenil , Fernando Soler-Toscano , Joost J. Joosten

The quest for quantum computers is motivated by their potential for solving problems that defy existing, classical, computers. The theory of computational complexity, one of the crown jewels of computer science, provides a rigorous…

Statistical Mechanics · Physics 2010-09-10 C. R. Laumann , R. Moessner , A. Scardicchio , S. L. Sondhi

A practical way to deal with the problem of time in quantum cosmology and quantum gravity is proposed. The main tool is effective equations, which mainly restrict explicit considerations to semiclassical regimes but have the crucial…

General Relativity and Quantum Cosmology · Physics 2011-07-20 Martin Bojowald , Philipp A Hoehn , Artur Tsobanjan

We formally define algorithmic capture of combinatorial tasks as the ability of a transformer to extrapolate to arbitrary task sizes with controllable error and logarithmic sample adaptation, providing a sharp scaling criterion for…

Machine Learning · Computer Science 2026-05-08 Orit Davidovich , Zohar Ringel
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