22 papers · ranked by Valyu relevance
Ashley Montanaro
Quantum computers are designed to outperform standard computers by running quantum algorithms. Areas in which quantum algorithms can be applied include cryptography, search and optimisation, simulation of quantum systems, and solving large systems of linear equations. Here we briefly survey some known quantum…
Amine Zeguendry, Zahi Jarir, Mohamed Quafafou, Andreas Wichert
Despite its undeniable success, classical machine learning remains a resource-intensive process. Practical computational efforts for training state-of-the-art models can now only be handled by high speed computer hardware. As this trend is expected to continue, it should come as no surprise that an increasing number of…
Federico Holik, Giuseppe Sergioli, Hector Freytes, Angel Plastino
In this work we advance a generalization of quantum computational logics capable of dealing with some important examples of quantum algorithms. We outline an algebraic axiomatization of these structures.
Shihao Zhang, Lvzhou Li
Quantum algorithms are demonstrated to outperform classical algorithms for certain problems and thus are promising candidates for efficient information processing. Herein we aim to provide a brief and popular introduction to quantum algorithms for both the academic community and the general public with interest. We…
Patrick J. Coles, Stephan Eidenbenz, Scott Pakin, Adetokunbo Adedoyin + 28 more
'Adetokunbo Adedoyin' 'John Ambrosiano' 'Petr M. Anisimov' 'William Casper' 'Gopinath Chennupati' 'Carleton Coffrin' 'Hristo Djidjev' 'David Gunter' 'Satish Karra' 'Nathan Lemons' 'Shizeng Lin' 'Andrey Y. Lokhov' 'Alexander Malyzhenkov' 'David Mascareñas' 'Susan M. Mniszewski' 'Balu Nadiga' 'Dan O’Malley' 'Diane Oyen'…
Ping Zhang, Durdu Guney, David Petrosyan
Shinagawa and Iwata are considered quantum security for the sum of Even-Mansour (SoEM) construction and provided quantum key recovery attacks by Simon’s algorithm and Grover’s algorithm. Furthermore, quantum key recovery attacks are also presented for natural generalizations of SoEM. For some variants of SoEM, they…
Ye-Chao Liu, Jiangwei Shang, Xiangdong Zhang
Besides the superior efficiency compared to their classical counterparts, quantum algorithms known so far are basically task-dependent, and scarcely any common features are shared between them. In this work, however, we show that the depletion of quantum coherence turns out to be a common phenomenon in these…
Rehab Elgendy, Ahmed Younes, H. M. Abu-Donia, R. M. Farouk
Analyzing the relations between Boolean functions has many applications in many fields, such as database systems, cryptography, and collision problems. This paper proposes four quantum algorithms that use amplitude amplification techniques to perform set operations, including Intersection, Difference, and Union, on two…
Authors not listed
We present a quantum algorithm for estimating the amplitude content of user-specified sequency bands in quantum-encoded signals. The method employs a sequency-ordered Quantum Walsh–Hadamard Transform (QWHT), a comparator-based oracle that coherently marks basis states within an arbitrary sequency range, and Quantum…
Kapil Kumar Soni, Akhtar Rasool, Siddhartha Bhattacharyya
This article presents efficient quantum solutions for exact multiple pattern matching to process the biological sequences. The classical solution takes Ο(mN) time for matching m patterns over N sized text database. The quantum search mechanism is a core for pattern matching, as this reduces time complexity and achieves…
BongJu Kim
We introduce quantum algorithm and the mathematical structure of quantum computer. Quantum algorithm is expressed by linear algebra on a finite dimensional complex inner product space. The mathematical formulations of quantum mechanics had been established in around 1930, by von Neumann. The formulation uses functional…
Authors not listed
The Hidden Subgroup Problem (HSP) unifies several landmark quantum algorithms, yet systematic exploration of its variants and modern applications has slowed. This paper revives HSP-based algorithm design by examining new group structures with direct relevance to post-quantum cryptography, lattice problems, and…
Christos Papalitsas, Ioannis Mouratidis, Michail Patsakis, Evangelos Stogiannos + 2 more
The exponential growth of publicly available genomic data has created unprecedented opportunities for sequence-based discovery. Locating specific k-mers is fundamental to diverse applications, including metagenomic classification, pathogen and cancer detection, and variant calling yet efficient identification of…
Koichi Miyamoto, Naoki Yamamoto, Yasubumi Sakakibara
We propose two quantum algorithms for a problem in bioinformatics, position weight matrix (PWM) matching, which aims to find segments (sequence motifs) in a biological sequence such as DNA and protein that have high scores defined by the PWM and are thus of informational importance related to biological function. The…
Zhihao Lan, WanZhen Liang
The variational quantum eigensolver (VQE) algorithm can simulate the chemical systems such as molecules in the noisy intermediate-scale quantum devices and shows promising applications in quantum chemistry simulations. The accuracy and computational cost of the VQE simulations are determined by the underlying Ansätze.…
Namasi G Sankar, Georgios Miliotis, Simon Caton
Genome assembly is important in infectious disease surveillance, antimicrobial resistance monitoring, and cancer genomics. The task of reconstructing full genomic sequences from fragmented reads, can be framed as a large scale combinatorial optimisation problem. Recent advances in quantum computing have introduced new…
Authors not listed
This work provides a rigorous theoretical investigation of selective error correction strategies for variational quantum algorithms, with focus on understanding the interplay between error suppression, circuit trainability, and computational resource requirements. We develop a mathematical framework that characterizes…
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Computational chemistry has entered a new era where machine learning (ML) models—particularly graph neural networks and machine learning force fields—routinely deliver quantum mechanical accuracy at classical speeds, scaling to millions of atoms and reshaping workflows in drug discovery, catalysis, and materials…
Mohadeseh Zarei Ghoabdi, Elaheh Afsaneh
Quantum machine learning algorithms using the power of quantum computing provide fast- developing approaches for solving complicated problems and speeding-up calculations for big data. As such, they could effectively operate better than the classical algorithms. Herein, we demonstrate for the first time the…
Patrícia Verdugo Pascoal, Deborah Bambil, Luisa Mayumi Arake de Tacca, Rayane Nunes Lima + 3 more
The accelerated exploration and engineering of nucleotide sequences are directed towards quantum mechanics and their intrinsic entanglements, implementing the qubits states, including the development of algorithms. The production rate of biological sequencing data has increased to approximately 1 Gb/h, but the ability…
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We develop a comprehensive theoretical framework for quantum-enhanced risk modeling in financial systems, establishing mathematical foundations for representing and computing risk factors using quantum states and operations. The theory begins by formulating portfolio risk as quantum observables, where correlations…
Authors not listed
This work establishes theoretical foundations for hierarchical quantum-classical algorithm design, where complex problems are decomposed across multiple spatial, temporal, or organizational scales with quantum and classical computation assigned to appropriate levels. We develop a mathematical framework that…