{"title":"Upper bound for output patterns of energy-bounded boolean circuits, and its applications","authors":"Jayalal Sarma, Kei Uchizawa","doi":"10.1007/s00236-026-00539-w","DOIUrl":null,"url":null,"abstract":"<div><p>Consider a Boolean circuit over a basis <span>\\(\\{ \\wedge , \\vee , \\lnot \\}\\)</span>, where <span>\\(\\wedge \\)</span> and <span>\\(\\vee \\)</span> are the conjunction of unbounded fan-in and disjunction of unbounded fan-in, respectively, and <span>\\(\\lnot \\)</span> is the negation. The energy complexity of a circuit is defined as the number of gates outputting ones in the circuit, where the maximum is taken over all the input assignments. \nWe prove that the number of output patterns (the set of possible outputs of all the internal gates in the circuit) in a circuit of energy <i>e</i> is at most <span>\\(2^{e \\log e + 4e}\\)</span> vastly improving the trivial bound of <span>\\(\\sum _{i=0}^e {s \\atopwithdelims ()i}\\)</span>. where <i>s</i> is the size of the circuit, which contrasts with our observation that a threshold circuit achieves the trivial bound. Building on this tool, we prove the following three applications: (i) Any Boolean circuit <i>C</i> of energy <i>e</i> can be converted to a depth-3 circuit of size <span>\\(s2^{e\\log e + 4e}\\)</span>. (ii) The problem of counting output patterns of a given circuit is known to be <span>\\(\\#\\textrm{P}\\)</span>-complete in general, but is fixed-parameter tractable when parameterized by energy <i>e</i> (over the basis <span>\\(\\mathcal {B}_2 = \\{\\wedge _2,\\vee _2,\\lnot \\}\\)</span>). We also show other FPT algorithms; (iii) If a function <span>\\(f:\\{0,1\\}^n \\times \\{0,1\\}^n \\rightarrow \\{0,1\\}\\)</span> is computed by a circuit of energy <i>e</i>, then the unbounded-error communication complexity <i>U</i>(<i>f</i>) is at most <span>\\(O(e \\log e)\\)</span>.</p></div>","PeriodicalId":7189,"journal":{"name":"Acta Informatica","volume":"63 3","pages":""},"PeriodicalIF":1.2000,"publicationDate":"2026-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Acta Informatica","FirstCategoryId":"94","ListUrlMain":"https://link.springer.com/article/10.1007/s00236-026-00539-w","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Consider a Boolean circuit over a basis \(\{ \wedge , \vee , \lnot \}\), where \(\wedge \) and \(\vee \) are the conjunction of unbounded fan-in and disjunction of unbounded fan-in, respectively, and \(\lnot \) is the negation. The energy complexity of a circuit is defined as the number of gates outputting ones in the circuit, where the maximum is taken over all the input assignments.
We prove that the number of output patterns (the set of possible outputs of all the internal gates in the circuit) in a circuit of energy e is at most \(2^{e \log e + 4e}\) vastly improving the trivial bound of \(\sum _{i=0}^e {s \atopwithdelims ()i}\). where s is the size of the circuit, which contrasts with our observation that a threshold circuit achieves the trivial bound. Building on this tool, we prove the following three applications: (i) Any Boolean circuit C of energy e can be converted to a depth-3 circuit of size \(s2^{e\log e + 4e}\). (ii) The problem of counting output patterns of a given circuit is known to be \(\#\textrm{P}\)-complete in general, but is fixed-parameter tractable when parameterized by energy e (over the basis \(\mathcal {B}_2 = \{\wedge _2,\vee _2,\lnot \}\)). We also show other FPT algorithms; (iii) If a function \(f:\{0,1\}^n \times \{0,1\}^n \rightarrow \{0,1\}\) is computed by a circuit of energy e, then the unbounded-error communication complexity U(f) is at most \(O(e \log e)\).
期刊介绍:
Acta Informatica provides international dissemination of articles on formal methods for the design and analysis of programs, computing systems and information structures, as well as related fields of Theoretical Computer Science such as Automata Theory, Logic in Computer Science, and Algorithmics.
Topics of interest include:
• semantics of programming languages
• models and modeling languages for concurrent, distributed, reactive and mobile systems
• models and modeling languages for timed, hybrid and probabilistic systems
• specification, program analysis and verification
• model checking and theorem proving
• modal, temporal, first- and higher-order logics, and their variants
• constraint logic, SAT/SMT-solving techniques
• theoretical aspects of databases, semi-structured data and finite model theory
• theoretical aspects of artificial intelligence, knowledge representation, description logic
• automata theory, formal languages, term and graph rewriting
• game-based models, synthesis
• type theory, typed calculi
• algebraic, coalgebraic and categorical methods
• formal aspects of performance, dependability and reliability analysis
• foundations of information and network security
• parallel, distributed and randomized algorithms
• design and analysis of algorithms
• foundations of network and communication protocols.