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

Journal of Qingdao University

THE KERNEL AND THE DEVELOPMENT OF QUANTUM COMPUTER

Yuan Yu-zhen

Published Jan 1, 2000• 0 citations
OpenAlex

Abstract

The paper analysis the difference and the sameness between quantum and classical computer, The advantage of quantum computer is parallel computation, The best computer would be composed of quantum and classical computer.

Related papers

DISCUSSION ON QUANTUM COMPUTER

GU Guo-tai

This paper first introduces the concept of quantum computer and some basic applications,then looks back the general research situation of quantum computer theory,explaining the basic quantum computer theory,the differences between quantum computer and classic computer,and at last points out the situations to realize quantum computer and the present difficulties.

Journal of Henan University of Technology•Jan 1, 2006•0 citations

Quantum Advantage in Machine Learning: A Comparative Study of Quantum and Classical Algorithms

Ramesh

Quantum computing holds the promise of revolutionizing various fields, including machine learning, by leveraging quantum phenomena to perform computations beyond the capabilities of classical computers. In this paper, we present a comparative study of quantum and classical algorithms in the context of machine learning tasks. We examine the advantages of quantum algorithms, such as quantum parallelism and entanglement, and their potential to outperform classical algorithms in tasks such as classification, clustering, and optimization. Through theoretical analysis and empirical experiments, we demonstrate the strengths and limitations of quantum machine learning algorithms, highlighting their potential for achieving quantum advantage in certain scenarios. Additionally, we discuss the challenges and opportunities for integrating quantum computing into machine learning pipelines and outline future research directions in this rapidly evolving field.

Journal of Quantum Science and Technology.•Mar 31, 2024•0 citations

Quantum Computation and Quantum Computer

Shen Zhen-kang

An introduction to the basic concept of quantum computation and quantum computer is presented in the paper, the advantages of quantum computer over the classical ones are analyzed. Finally some proposed methods of realization and application of the quantum computer are briefly discussed.

Optoelectronic Technology & Information•Jan 1, 2003•0 citations

DEVELOPMENT OF HYBRID QUANTUM-CLASSICAL MODELS FOR COMPUTER VISION

Aksultan Mukhanbet, Nurtugan Azatbekuly, Beimbet Daribayev

This research explores the integration of quantum computing with classical machine learning to enhance data classification tasks using Quantum Neural Networks (QNN) and Parameterized Quantum Circuits (PQC). The hybrid approach leverages the advantages of both quantum and classical systems to improve the efficiency and accuracy of data processing. In this model, data is encoded into qubits using amplitude encoding, representing input vectors as amplitudes of quantum states. The QNN is initialized by placing the qubits in superposition using Hadamard gates, followed by data encoding with parameterized rotational gates that map classical data to quantum states using rotation angles. PQC plays a central role by applying layers of parameterized quantum operations to process data in the quantum space. These parameters are optimized during the training process, where a quadratic loss function minimizes the error between the predicted quantum states and the true class labels using gradient descent. Experiments conducted on the MNIST dataset show that the hybrid the hybrid quantum-classical neural network (QCNN) with PQC achieves a classification accuracy of over 95%, highlighting its potential in machine learning applications. The results demonstrate that integrating quantum computing with classical machine learning enhances performance in complex data analysis tasks due to the exponential growth of quantum state space and the parallelism of quantum systems, making hybrid models promising for computer vision and classification tasks.

Journal of problem in computer science and information technologies•Apr 3, 2025•1 citation

ASAS-NANP SYMPOSIUM: MATHEMATICAL MODELING IN ANIMAL NUTRITION: quantum computing in agricultural sciences: from theory to reality

Luis Orlindo Tedeschi

Quantum computing (QC) represents a revolutionary paradigm in information processing, leveraging quantum mechanical phenomena (superposition, entanglement, quantum interference, and quantum tunneling) to perform calculations in fundamentally different ways than classical computing (CC). While CC processes information sequentially through Boolean logic operations on discrete binary states (0 s and 1 s), quantum computers manipulate qubits that can exist in superpositions of states, enabling parallel operations on exponentially large state spaces. Despite claims regarding "quantum supremacy," QC remains in its early developmental stages, comparable to the CC of the 1950s and 1960s. True quantum supremacy, where quantum computers demonstrate definitive, practical advantages over classical computers for well-defined tasks, has not yet been established. Practical applications face real challenges, i.e., decoherence, high error rates, and demanding error correction requirements. Three developmental phases are projected: noisy intermediate-scale quantum systems by 2030, broad quantum advantage from 2030 to 2040, and full-scale fault tolerance after 2040. Does QC offer solutions to fundamental problems that classical systems, including supercomputers and artificial intelligence, cannot already resolve? While conventional technologies continue to advance agricultural capabilities through machine learning (ML) and complex optimization, quantum approaches may potentially transform domains that require molecular-level simulations (such as soil chemistry and rumen microbial interactions) or exponentially complex optimization problems in resource allocation. Quantum ML models, such as quantum neural networks, generative adversarial networks, and autoencoders, are being explored in quantum-classical hybrids, which have shown potential for faster optimization and higher-dimensional data representation; but, these advantages remain largely conceptual. The value proposition of QC in agriculture ultimately depends on whether the field's most pressing challenges involve quantum mechanical processes that classical computers cannot simulate efficiently or optimization problems of such complexity that quantum algorithms would provide substantial practical advantages over classical approaches. The agricultural community must also address societal implications, such as access equity, data ownership, algorithmic transparency, and educational preparedness for this emerging technology.

Journal of Animal Science•Dec 17, 2025•4 citations

Principles and Characteristics of Quantum Computing

Dianhao Liu

Abstract With the rapid development of science and technology, people tend to have a higher demand for computing. Classical computers can no longer meet part of the computing needs, so quantum information systems have gained more and more attention. Quantum computers have good parallelism, fast information processing speed, large amount of information storage, and performance in many fields far surpasses classical computers. In this article, the author introduces coherent superposition and part of quantum gates and quantum algorithms meanwhile compares classical computers with quantum computers in many aspects. The advantages and disadvantages of quantum computers in principle are also drawn out. Finally, the author proposes future prospects for quantum computers prediction.

Journal of Physics Conference Series•Sep 1, 2021•2 citations
Quantum computing holds the promise of revolutionizing various fields, including machine learning, by leveraging quantum phenomena to perform computations beyond the capabilities of classical computers. In this paper, we present a comparative study of quantum and classical algorithms in the context of machine learning tasks. We examine the advantages of quantum algorithms, such as quantum parallelism and entanglement, and their potential to outperform classical algorithms in tasks such as classification, clustering, and optimization. Through theoretical analysis and empirical experiments, we demonstrate the strengths and limitations of quantum machine learning algorithms, highlighting their potential for achieving quantum advantage in certain scenarios. Additionally, we discuss the challenges and opportunities for integrating quantum computing into machine learning pipelines and outline future research directions in this rapidly evolving field.
This research explores the integration of quantum computing with classical machine learning to enhance data classification tasks using Quantum Neural Networks (QNN) and Parameterized Quantum Circuits (PQC). The hybrid approach leverages the advantages of both quantum and classical systems to improve the efficiency and accuracy of data processing. In this model, data is encoded into qubits using amplitude encoding, representing input vectors as amplitudes of quantum states. The QNN is initialized by placing the qubits in superposition using Hadamard gates, followed by data encoding with parameterized rotational gates that map classical data to quantum states using rotation angles. PQC plays a central role by applying layers of parameterized quantum operations to process data in the quantum space. These parameters are optimized during the training process, where a quadratic loss function minimizes the error between the predicted quantum states and the true class labels using gradient descent. Experiments conducted on the MNIST dataset show that the hybrid the hybrid quantum-classical neural network (QCNN) with PQC achieves a classification accuracy of over 95%, highlighting its potential in machine learning applications. The results demonstrate that integrating quantum computing with classical machine learning enhances performance in complex data analysis tasks due to the exponential growth of quantum state space and the parallelism of quantum systems, making hybrid models promising for computer vision and classification tasks.
Quantum computing (QC) represents a revolutionary paradigm in information processing, leveraging quantum mechanical phenomena (superposition, entanglement, quantum interference, and quantum tunneling) to perform calculations in fundamentally different ways than classical computing (CC). While CC processes information sequentially through Boolean logic operations on discrete binary states (0 s and 1 s), quantum computers manipulate qubits that can exist in superpositions of states, enabling parallel operations on exponentially large state spaces. Despite claims regarding "quantum supremacy," QC remains in its early developmental stages, comparable to the CC of the 1950s and 1960s. True quantum supremacy, where quantum computers demonstrate definitive, practical advantages over classical computers for well-defined tasks, has not yet been established. Practical applications face real challenges, i.e., decoherence, high error rates, and demanding error correction requirements. Three developmental phases are projected: noisy intermediate-scale quantum systems by 2030, broad quantum advantage from 2030 to 2040, and full-scale fault tolerance after 2040. Does QC offer solutions to fundamental problems that classical systems, including supercomputers and artificial intelligence, cannot already resolve? While conventional technologies continue to advance agricultural capabilities through machine learning (ML) and complex optimization, quantum approaches may potentially transform domains that require molecular-level simulations (such as soil chemistry and rumen microbial interactions) or exponentially complex optimization problems in resource allocation. Quantum ML models, such as quantum neural networks, generative adversarial networks, and autoencoders, are being explored in quantum-classical hybrids, which have shown potential for faster optimization and higher-dimensional data representation; but, these advantages remain largely conceptual. The value proposition of QC in agriculture ultimately depends on whether the field's most pressing challenges involve quantum mechanical processes that classical computers cannot simulate efficiently or optimization problems of such complexity that quantum algorithms would provide substantial practical advantages over classical approaches. The agricultural community must also address societal implications, such as access equity, data ownership, algorithmic transparency, and educational preparedness for this emerging technology.
Abstract With the rapid development of science and technology, people tend to have a higher demand for computing. Classical computers can no longer meet part of the computing needs, so quantum information systems have gained more and more attention. Quantum computers have good parallelism, fast information processing speed, large amount of information storage, and performance in many fields far surpasses classical computers. In this article, the author introduces coherent superposition and part of quantum gates and quantum algorithms meanwhile compares classical computers with quantum computers in many aspects. The advantages and disadvantages of quantum computers in principle are also drawn out. Finally, the author proposes future prospects for quantum computers prediction.