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Entropy
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Quantum Computing for Complex Dynamics, 2nd Edition
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Quantum Computing for Complex Dynamics, 2nd Edition

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Quantum Information".

Deadline for manuscript submissions: closed (30 December 2024) | Viewed by 4337

Special Issue Editors

Dr. Heng Fan

E-Mail Website
Guest Editor
Key Laboratory of Condensed Matter Theory and Computation, Institute of Physics, Chinese Academy of Sciences, Beijing, China
Interests: quantum computing and quantum information; quantum information processing applications in condensed matter physics; strongly correlated condensed matter systems; statistical models and quantum field theory
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Guilu Long

E-Mail Website
Guest Editor
State Key Laboratory of Low-Dimensional Quantum Physics and Department of Physics, Tsinghua University, Beijing 100084, China
Interests: quantum communication; quantum computation; quantum information; quantum secure direct communication; quantum algorithm
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

During the 1980s, physicists combined a quantum mechanical model with computer science, producing quantum computers. These quantum computers could perform much better than a classical computer. Since then, the research on quantum computation has been growing rapidly, both in architecture and algorithms.

Complex dynamics are known for their complexity, chaos, and randomness, which widely exist in the field of cryptography, communication, chemistry, and so on. It is hard for classic computers to deal with complex dynamics, while quantum computers act as an ideal tool with which to calculate and simulate them.

This Special Issue mainly focuses on the state of the art of the research on quantum computation and quantum algorithms, particularly that on the computation of the complex dynamics. The topics include, but are not limited to, the following:

  • Quantum algorithms;
  • Quantum circuits;
  • Quantum communication;
  • Quantum computing;
  • Quantum cryptography;
  • Quantum computation;
  • Quantum computer architecture;
  • Quantum information;
  • Quantum machine learning;
  • Quantum networks and communication;
  • Quantum programming;
  • Quantum simulation;
  • Complex dynamics;
  • Open quantum dynamics;
  • Computational complexity;
  • Quantum chaos;
  • Quantum complexity theory;
  • Quantum maps;
  • Quantum dots.

Dr. Heng Fan
Prof. Dr. Guilu Long
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • quantum algorithms
  • quantum circuits
  • quantum communication
  • quantum computing
  • quantum cryptography
  • quantum computation

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Related Special Issue

Published Papers (4 papers)

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Research

17 pages, 690 KiB  
Article
Quantifying Unknown Multiqubit Entanglement Using Machine Learning
by Yukun Wang, Shaoxuan Wang, Jincheng Xing, Yuxuan Du and Xingyao Wu
Entropy 2025, 27(2), 185; https://doi.org/10.3390/e27020185 - 12 Feb 2025
Viewed by 767
Abstract
Entanglement plays a pivotal role in numerous quantum applications, and as technology progresses, entanglement systems continue to expand. However, quantifying entanglement is a complex problem, particularly for multipartite quantum states. The currently available entanglement measures suffer from high computational complexity, and for unknown [...] Read more.
Entanglement plays a pivotal role in numerous quantum applications, and as technology progresses, entanglement systems continue to expand. However, quantifying entanglement is a complex problem, particularly for multipartite quantum states. The currently available entanglement measures suffer from high computational complexity, and for unknown multipartite entangled states, complete information about the quantum state is often necessary, further complicating calculations. In this paper, we train neural networks to quantify unknown multipartite entanglement using input features based on squared entanglement (SE) and outcome statistics data produced by locally measuring target quantum states. By leveraging machine learning techniques to handle non-linear relations between outcome statistics and entanglement measurement SE, we achieve high-precision quantification of unknown multipartite entanglement states with a linear number of measurements, avoiding the need for global measurements and quantum state tomography. The proposed method exhibits robustness against noise and extends its applicability to pure and mixed states, effectively scaling to large-scale multipartite entanglement systems. The results of the experiment show that the predicted entanglement measures are very close to the actual values, which confirms the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Quantum Computing for Complex Dynamics, 2nd Edition)
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16 pages, 1170 KiB  
Article
Image Similarity Quantum Algorithm and Its Application in Image Retrieval Systems
by Qingchuan Yang, Xianing Feng and Lianfu Wei
Entropy 2025, 27(2), 137; https://doi.org/10.3390/e27020137 - 27 Jan 2025
Viewed by 875
Abstract
The measurement of image similarity represents a fundamental task within the domain of image processing, enabling the application of sophisticated computational techniques to ascertain the degree of similarity between two images. To enhance the performance of these similarity measurement algorithms, the academic community [...] Read more.
The measurement of image similarity represents a fundamental task within the domain of image processing, enabling the application of sophisticated computational techniques to ascertain the degree of similarity between two images. To enhance the performance of these similarity measurement algorithms, the academic community has investigated a range of quantum algorithms. Notably, the swap test-based quantum inner product algorithm (ST-QIP) has emerged as a pivotal method for computing image similarity. However, the inherent destructive nature of the swap test necessitates multiple quantum state evolutions and measurements, which leads to consumption of quantum resources and prolonged computational time, ultimately constraining its practical applicability. To address these limitations, this study introduces an advanced quantum inner product algorithm based on amplitude estimation (AE-QIP) designed to compute image similarity. This innovative approach circumvents the repetitive measurement processes associated with the swap test, thereby optimizing the utilization of quantum resources and substantially enhancing the algorithm’s performance. We conducted experiments using a quantum simulator to implement the AE-QIP algorithm and evaluate its effectiveness in the image retrieval tasks. It is found that the AE-QIP algorithm achieves comparable precision to the ST-QIP algorithm while exhibiting significant reductions in qubit consumption and average processing time. Additionally, our findings suggest that increasing the number of ancillary qubits can further enhance the accuracy of the AE-QIP algorithm. Overall, within the acceptable error thresholds, the AE-QIP algorithm exhibits enhanced efficiency relative to the ST-QIP algorithm. However, significant further research is needed to address the challenges involved in optimizing the performance of quantum retrieval systems as a whole. Full article
(This article belongs to the Special Issue Quantum Computing for Complex Dynamics, 2nd Edition)
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18 pages, 850 KiB  
Article
A Hybrid Quantum Solver for the Lorenz System
by Sajad Fathi Hafshejani, Daya Gaur, Arundhati Dasgupta, Robert Benkoczi, Narasimha Reddy Gosala and Alfredo Iorio
Entropy 2024, 26(12), 1009; https://doi.org/10.3390/e26121009 - 22 Nov 2024
Cited by 1 | Viewed by 956
Abstract
We develop a hybrid classical–quantum method for solving the Lorenz system. We use the forward Euler method to discretize the system in time, transforming it into a system of equations. This set of equations is solved by using the Variational Quantum Linear Solver [...] Read more.
We develop a hybrid classical–quantum method for solving the Lorenz system. We use the forward Euler method to discretize the system in time, transforming it into a system of equations. This set of equations is solved by using the Variational Quantum Linear Solver (VQLS) algorithm. We present numerical results comparing the hybrid method with the classical approach for solving the Lorenz system. The simulation results demonstrate that the VQLS method can effectively compute solutions comparable to classical methods. The method is easily extended to solving similar nonlinear differential equations. Full article
(This article belongs to the Special Issue Quantum Computing for Complex Dynamics, 2nd Edition)
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12 pages, 4146 KiB  
Article
Infidelity Analysis of Digital Counter-Diabatic Driving in Simple Two-Qubit System
by Ouyang Lei
Entropy 2024, 26(10), 877; https://doi.org/10.3390/e26100877 - 19 Oct 2024
Viewed by 994
Abstract
Digitized counter-diabatic (CD) optimization algorithms have been proposed and extensively studied to enhance performance in quantum computing by accelerating adiabatic processes while minimizing energy transitions. While adding approximate counter-diabatic terms can initially introduce adiabatic errors that decrease over time, Trotter errors from decomposition [...] Read more.
Digitized counter-diabatic (CD) optimization algorithms have been proposed and extensively studied to enhance performance in quantum computing by accelerating adiabatic processes while minimizing energy transitions. While adding approximate counter-diabatic terms can initially introduce adiabatic errors that decrease over time, Trotter errors from decomposition approximation persist. On the other hand, increasing the high-order nested commutators for CD terms may improve adiabatic errors but could also introduce additional Trotter errors. In this article, we examine the two-qubit model to explore the interplay between approximate CD, adiabatic errors, Trotter errors, coefficients, and commutators. Through these analyses, we aim to gain insights into optimizing these factors for better fidelity, a shallower circuit depth, and a reduced gate number in near-term gate-based quantum computing. Full article
(This article belongs to the Special Issue Quantum Computing for Complex Dynamics, 2nd Edition)
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