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We present a scalable, robust approach to creating quantum programs of arbitrary size and complexity. The approach is based on the true abstraction of the problem. The quantum program is expressed in terms of a high-level model together…

The rapid development of artificial intelligence (AI) systems has created an urgent need for their scientific quantification. While their fluency across a variety of domains is impressive, AI systems fall short on tests requiring…

Artificial Intelligence · Computer Science 2025-06-23 Takuya Ito , Murray Campbell , Lior Horesh , Tim Klinger , Parikshit Ram

Any particular classical system and its quantum version are normally viewed as separate formulations that are strictly distinct. Our goal is to overcome the two separate languages and create a smooth and common procedure that provides a…

Quantum Physics · Physics 2020-10-13 John R. Klauder

It is becoming increasingly clear that, if a useful device for quantum computation will ever be built, it will be embodied by a classical computing machine with control over a truly quantum subsystem, this apparatus performing a mixture of…

Programming Languages · Computer Science 2009-11-07 S. Bettelli , L. Serafini , T. Calarco

E-generalization computes common generalizations of given ground terms w.r.t. a given equational background theory E. In 2005 [arXiv:1403.8118], we had presented a computation approach based on standard regular tree grammar algorithms, and…

Logic in Computer Science · Computer Science 2017-09-05 Jochen Burghardt

This work presents an analytical framework for the design and analysis of LLM-based algorithms, i.e., algorithms that contain one or multiple calls of large language models (LLMs) as sub-routines and critically rely on the capabilities of…

Machine Learning · Computer Science 2025-10-14 Yanxi Chen , Yaliang Li , Bolin Ding , Jingren Zhou

We propose a calculus for modeling implicit programming that supports first-class, overlapping, locally scoped, and higher-order instances with higher-kinded types. We propose a straightforward generalization of the well-established System…

Programming Languages · Computer Science 2025-04-01 Eugène Flesselle

This article contains ideas and their elaboration for quantifiers, which appeared after checking in practice the experimental language of the formal knowledge representation YAFOLL [1]: - looking at for_all and exists quantifiers as…

Logic in Computer Science · Computer Science 2019-08-30 Alex Shkotin

Many interesting and useful symbolic computation algorithms manipulate mathematical expressions in mathematically meaningful ways. Although these algorithms are commonplace in computer algebra systems, they can be surprisingly difficult to…

Logic in Computer Science · Computer Science 2019-05-07 Jacques Carette , William M. Farmer

Search-optimization problems are plentiful in scientific and engineering domains. Artificial intelligence has long contributed to the development of search algorithms and declarative programming languages geared towards solving and modeling…

Artificial Intelligence · Computer Science 2022-06-17 Yuliya Lierler

We formalize a new paradigm for optimality of algorithms, that generalizes worst-case optimality based only on input-size to problem-dependent parameters including implicit ones. We re-visit some existing sorting algorithms from this…

Data Structures and Algorithms · Computer Science 2025-11-11 Sandeep Sen

Here we define a new unification algorithm for terms interpreted in semantic domains denoted by a subclass of regular types here called deterministic regular types. This reflects our intention not to handle the semantic universe as a…

Logic in Computer Science · Computer Science 2025-02-14 João Barbosa , Mário Florido , Vítor Santos Costa

This paper discusses to which extent the concept of ``anytime algorithms'' can be applied to parsing algorithms with feature unification. We first try to give a more precise definition of what an anytime algorithm is. We arque that parsing…

cmp-lg · Computer Science 2008-02-03 Guenther Goerz , Marcus Kesseler

We provide a "shared axiomatization" of natural numbers and hereditarily finite sets built around a polymorphic abstraction of bijective base-2 arithmetics. The "axiomatization" is described as a progressive refinement of Haskell type…

Symbolic Computation · Computer Science 2010-07-01 Paul Tarau

Synthesizing programs from examples requires searching over a vast, combinatorial space of possible programs. In this search process, a key challenge is representing the behavior of a partially written program before it can be executed, to…

Programming Languages · Computer Science 2021-04-21 Maxwell Nye , Yewen Pu , Matthew Bowers , Jacob Andreas , Joshua B. Tenenbaum , Armando Solar-Lezama

This paper presents a novel approach to including non-instantaneous discrete control transitions in the linear hybrid automaton approach to simulation and verification of hybrid control systems. In this paper we study the control of a…

Systems and Control · Computer Science 2015-01-26 Avinash Malik , Partha Roop

We show that lambda calculus is a computation model which can step by step simulate any sequential deterministic algorithm for any computable function over integers or words or any datatype. More formally, given an algorithm above a family…

Logic in Computer Science · Computer Science 2010-10-15 Marie Ferbus-Zanda , Serge Grigorieff

Modal automata are a classic formal model for component-based systems that comes equipped with a rich specification theory supporting abstraction, refinement and compositional reasoning. In recent years, quantitative variants of modal…

Logic in Computer Science · Computer Science 2013-06-13 Tingting Han , Christian Krause , Marta Kwiatkowska , Holger Giese

Analog computers can be revived as a feasible technology platform for low precision, energy efficient and fast computing. We justify this statement by measuring the performance of a modern analog computer and comparing it with that of…

Computational Physics · Physics 2021-12-20 Sven Köppel , Bernd Ulmann , Lars Heimann , Dirk Killat

Developing robust representations of chemical structures that enable models to learn topological inductive biases is challenging. In this manuscript, we present a representation of atomistic systems. We begin by proving that our…

Machine Learning · Computer Science 2024-09-27 Rahul Khorana , Marcus Noack , Jin Qian
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