Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Cybernetics and Systems Analysis

Quantum model of computations: Underlying principles and achievements

Anatoly V. Anisimov, S. V. Danil’chenko

Published Jan 1, 2000• 1 citation
Publisher
DOI
10.1007/bf02733301

Related papers

基于量子纠缠的拓扑学几何的自适应建模

Jincheng Zhang

This paper presents a novel approach to geometry modeling utilizing quantum entanglement. The core concept revolves around a dynamically adjustable, quantum-enhanced modeling framework that adapts to input data, allowing for unprecedented precision and efficiency in geometric prediction. We explore the potential of this method to overcome limitations in traditional geometric modeling techniques, particularly in scenarios requiring high accuracy and complex transformations. The paper details the underlying principles of entanglement-based geometry and introduces a robust modeling algorithm, demonstrating its effectiveness through illustrative examples and preliminary results. The focus is on achieving a self-adaptive and computationally efficient method for geometric representation.

•Aug 31, 2026•0 citations

Quantum Computation and Its Perspective

Craig S. Lent

FEDジャ-ナル•Mar 1, 1997•2 citations

Models of quantum computation

Mingsheng Ying

Elsevier eBooks•Jan 1, 2024•0 citations

Quantum Entanglement-Based Nonlinear Topology Model

Jincheng Zhang

This paper introduces a novel approach to topology modeling leveraging the principles of quantum entanglement. We propose a framework wherein the process of constructing topological structures is fundamentally linked to quantum entanglement, enabling precise geometric representation and accurate modeling of complex topological relationships. The core mechanism centers on utilizing entanglement to fundamentally alter the computational landscape of topology, offering a new method for geometric manipulation and precise representation. This work addresses limitations of traditional topological modeling by introducing a quantum-enhanced approach that surpasses existing methods in its ability to capture intricate non-linear interactions within topological spaces. The resulting model demonstrates the potential for achieving significantly improved geometric accuracy and a deeper understanding of the underlying topological dynamics.

•Sep 4, 2026•0 citations

QCCP: a taskflow programming model for emerging computing scenario

Qiming Du, Jinchen Xu, Yu Zhu +6 more

As the demand for computing power continues to rise, it is difficult for a single type of computing device or architecture to satisfy the current situation. Diversity and heterogeneity are becoming more and more popular. Seamlessly integrating the realms of high performance computing and quantum computing, and harnessing their collective potential, has emerged as a consensus approach to effectively address the pressing need for increased computing power. In the emerging computing scenario, various different types of computing devices have super-heterogeneous characteristics, and there are significant differences in computational principles, programming models, parallelism, etc. Effectively harnessing these disparate resources and achieving a unified programming paradigm have become urgent imperatives. To address the above problems, this paper introduces QCCP, a taskflow programming model that enables efficient collaborative computing between classical computers and quantum computers. QCCP establishes a unified programming abstraction, shields the super-heterogeneous characteristics of the underlying network and hardware, and supports flexible scheduling for different computational backends. The experimental results indicate that QCCP can support the processing of hybrid classical-quantum applications with diverse program structures. In particular, QCCP reveals its immense potential and superiority in tackling real-world challenges, specifically in the realm of quantum circuit cutting and reconstruction.

EPJ Quantum Technology•Feb 13, 2025•1 citation

Models for Quantum Computation

Peter P. Rohde

Cambridge University Press eBooks•Sep 9, 2021•0 citations
This paper presents a novel approach to geometry modeling utilizing quantum entanglement. The core concept revolves around a dynamically adjustable, quantum-enhanced modeling framework that adapts to input data, allowing for unprecedented precision and efficiency in geometric prediction. We explore the potential of this method to overcome limitations in traditional geometric modeling techniques, particularly in scenarios requiring high accuracy and complex transformations. The paper details the underlying principles of entanglement-based geometry and introduces a robust modeling algorithm, demonstrating its effectiveness through illustrative examples and preliminary results. The focus is on achieving a self-adaptive and computationally efficient method for geometric representation.
This paper introduces a novel approach to topology modeling leveraging the principles of quantum entanglement. We propose a framework wherein the process of constructing topological structures is fundamentally linked to quantum entanglement, enabling precise geometric representation and accurate modeling of complex topological relationships. The core mechanism centers on utilizing entanglement to fundamentally alter the computational landscape of topology, offering a new method for geometric manipulation and precise representation. This work addresses limitations of traditional topological modeling by introducing a quantum-enhanced approach that surpasses existing methods in its ability to capture intricate non-linear interactions within topological spaces. The resulting model demonstrates the potential for achieving significantly improved geometric accuracy and a deeper understanding of the underlying topological dynamics.
As the demand for computing power continues to rise, it is difficult for a single type of computing device or architecture to satisfy the current situation. Diversity and heterogeneity are becoming more and more popular. Seamlessly integrating the realms of high performance computing and quantum computing, and harnessing their collective potential, has emerged as a consensus approach to effectively address the pressing need for increased computing power. In the emerging computing scenario, various different types of computing devices have super-heterogeneous characteristics, and there are significant differences in computational principles, programming models, parallelism, etc. Effectively harnessing these disparate resources and achieving a unified programming paradigm have become urgent imperatives. To address the above problems, this paper introduces QCCP, a taskflow programming model that enables efficient collaborative computing between classical computers and quantum computers. QCCP establishes a unified programming abstraction, shields the super-heterogeneous characteristics of the underlying network and hardware, and supports flexible scheduling for different computational backends. The experimental results indicate that QCCP can support the processing of hybrid classical-quantum applications with diverse program structures. In particular, QCCP reveals its immense potential and superiority in tackling real-world challenges, specifically in the realm of quantum circuit cutting and reconstruction.