English
Related papers

Related papers: A Quantum Algorithm for the Classification of Patt…

200 papers

Canalizing functions have important applications in physics and biology. For example, they represent a mechanism capable of stabilizing chaotic behavior in Boolean network models of discrete dynamical systems. When comparing the class of…

Mathematical Physics · Physics 2009-11-10 Winfried Just , Ilya Shmulevich , John Konvalina

We describe the construction of quantum gates (unitary operators) from boolean functions and give a number of applications. Both non-reversible and reversible boolean functions are considered. The construction of the Hamilton operator for a…

Mathematical Software · Computer Science 2015-01-05 Yorick Hardy , Willi-Hans Steeb

Quantum classification is defined as the task of predicting the associated class of an unknown quantum state drawn from an ensemble of pure states given a finite number of copies of this state. By recasting the state discrimination problem…

Quantum Physics · Physics 2016-08-14 Sébastien Gambs

An ordered binary decision diagram (OBDD) is a directed acyclic graph that represents a Boolean function. OBDDs are also known as special cases of oblivious read-once branching programs in the field of complexity theory. Since OBDDs have…

Quantum Physics · Physics 2025-05-19 Seiichiro Tani

We show that arbitrary functions of continuous variables, e.g. position and momentum, can be used to generate tests that distinguish quantum theory from local hidden variable theories. By optimising these functions, we obtain more robust…

Quantum Physics · Physics 2009-11-16 Q. Y. He , E. G. Cavalcanti , M. D. Reid , P. D. Drummond

Anomaly detection is a crucial task in machine learning that involves identifying unusual patterns or events in data. It has numerous applications in various domains such as finance, healthcare, and cybersecurity. With the advent of quantum…

Quantum Physics · Physics 2023-11-07 Julien Mellaerts

We present a quantum algorithm for systems of (possibly inhomogeneous) linear ordinary differential equations with constant coefficients. The algorithm produces a quantum state that is proportional to the solution at a desired final time.…

Quantum Physics · Physics 2017-11-07 Dominic W. Berry , Andrew M. Childs , Aaron Ostrander , Guoming Wang

We define and study the complexity of robust polynomials for Boolean functions and the related fault-tolerant quantum decision trees, where input bits are perturbed by noise. We compare several different possible definitions. Our main…

Quantum Physics · Physics 2007-05-23 Harry Buhrman , Ilan Newman , Hein Roehrig , Ronald de Wolf

There have been several research works on the hidden shift problem, quantum algorithms for the problem, and their applications. However, all the results have focused on discrete groups with discrete oracle functions. In this paper, we…

Quantum Physics · Physics 2021-10-28 Eunok Bae , Soojoon Lee

We study the average case approximation of the Boolean mean by quantum algorithms. We prove general query lower bounds for classes of probability measures on the set of inputs. We pay special attention to two probabilities, where we show…

Quantum Physics · Physics 2007-05-23 A. Papageorgiou

We study the power of nonadaptive quantum query algorithms, which are algorithms whose queries to the input do not depend on the result of previous queries. First, we show that any bounded-error nonadaptive quantum query algorithm that…

Quantum Physics · Physics 2010-12-20 Ashley Montanaro

Accurately estimating high-order moments of quantum states is an elementary precondition for many crucial tasks in quantum computing, such as entanglement spectroscopy, entropy estimation, spectrum estimation, and predicting non-linear…

Quantum Physics · Physics 2024-05-16 Benchi Zhao , Mingrui Jing , Lei Zhang , Xuanqiang Zhao , Yu-Ao CHen , Kun Wang , Xin Wang

Here we revisit the quantum algorithms for obtaining Forrelation [Aaronson et al, 2015] values to evaluate some of the well-known cryptographically significant spectra of Boolean functions, namely the Walsh spectrum, the cross-correlation…

Quantum Physics · Physics 2025-05-20 Suman Dutta , Subhamoy Maitra , Chandra Sekhar Mukherjee

A quantum single-error-correcting scheme can be derived from a one-way entanglement purification protocol in purifying one Bell state from a finite block of five Bell states. The main issue to be concerned with in the theory of such an…

Quantum Physics · Physics 2007-05-23 Jin-Yuan Hsieh , Che-Ming Li

A new class of functions is presented. The structure of the algorithm, particularly the selection criteria (branching), is used to define the fundamental property of the new class. The most interesting property of the new functions is that…

Computational Complexity · Computer Science 2020-02-25 Rade Vuckovac

Imbalance learning is a subfield of machine learning that focuses on learning tasks in the presence of class imbalance. Nearly all existing studies refer to class imbalance as a proportion imbalance, where the proportion of training samples…

Machine Learning · Computer Science 2023-05-09 Ou Wu

Decision of whether a Boolean equation system has a solution is an NPC problem and finding a solution is NP hard. In this paper, we present a quantum algorithm to decide whether a Boolean equation system FS has a solution and compute one if…

Quantum Physics · Physics 2018-08-07 Yu-Ao Chen , Xiao-Shan Gao

This paper considers the quantum query complexity of {\it $\eps$-biased oracles} that return the correct value with probability only $1/2 + \eps$. In particular, we show a quantum algorithm to compute $N$-bit OR functions with…

Quantum Physics · Physics 2007-05-23 Tomoya Suzuki , Shigeru Yamashita , Masaki Nakanishi , Katsumasa Watanabe

We pursue a systematic study of the following problem. Let f:{0,1}^n -> {0,1} be a (usually monotone) Boolean function whose behaviour is well understood when the input bits are identically independently distributed. What can be said about…

Probability · Mathematics 2012-01-17 Itai Benjamini , Ori Gurel-Gurevich , Ron Peled

This paper studies a machine learning regression problem as a multivariate approximation problem using the framework of the theory of random functions. An ab initio derivation of a regression method is proposed, starting from postulates of…

Machine Learning · Computer Science 2025-12-16 Yuriy N. Bakhvalov