An Improved Noise Quantum Annealing Method for TSP

Yang, Dong; Huang, Zhijie

doi:10.1007/s10773-020-04628-5

Cited by 9 publications

(9 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Those investigated using at least three methods are only 5 which include machine learning, graphics, routing, scheduling, and portfolio domains. Moreover, some case studies with the same specific problem like the Traveling Salesman Problem were analyzed using different methods which include QA [72,127] and improved QA [150]. This means further analysis and exploration of QA methods to be implemented in different/same domains and case studies can become an interesting challenge in the future.…”

Section: ) Results Reporting On Rq2mentioning

confidence: 99%

Implementation of Quantum Annealing: A Systematic Review

Yulianti

Surendro

2022

IEEE Access

View full text Add to dashboard Cite

Quantum annealing is a quantum computing approach widely used for optimization and probabilistic sampling problems. It is an alternative approach designed due to the limitations of gate-based quantum computing models. The method is observed to have a significant impact on different fields such as machine learning, graphics, routing, scheduling, computational chemistry, computational biology, security, portfolio, and others despite the fact that it is relatively new. This research provides a systematic review of research development trends in the field of quantum annealing and analyzes how it has been implemented in different problem domains. The results are expected to serve as the basis to identify the opportunities and challenges of research related to its implementation. The main contribution of this systematic review is to summarize different implementations of quantum annealing. It is also to analyze the prospect and opportunities in one of the problem domains with the greatest interest which is machine learning.

show abstract

Section: ) Results Reporting On Rq2mentioning

confidence: 99%

Implementation of Quantum Annealing: A Systematic Review

Yulianti

Surendro

2022

IEEE Access

View full text Add to dashboard Cite

show abstract

“…This led finally to the quantum extension or to the invention of the quantum annealing technique, where our initial contributions (References [ 23 , 40 ]) are considered to be important and pioneering. See, e.g., Reference [ 24 ] for a brief review and Reference [ 44 ] for some recent discussions on the advantages of applying quantum annealing method to solve TSP. Quantum annealing is a very active research field today in quantum statistical physics and computation (see, e.g., References [ 45 , 46 ] for recent reviews).…”

Section: Major Achievements and Publications Of The ‘Kolkata Schoomentioning

confidence: 99%

Development of Econophysics: A Biased Account and Perspective from Kolkata

Chakrabarti

Sinha

2021

Entropy

View full text Add to dashboard Cite

We present here a somewhat personalized account of the emergence of econophysics as an attractive research topic in physical, as well as social, sciences. After a rather detailed storytelling about our endeavors from Kolkata, we give a brief description of the main research achievements in a simple and non-technical language. We also briefly present, in technical language, a piece of our recent research result. We conclude our paper with a brief perspective.

show abstract

“…The quantum annealing algorithm based on quantum tunneling effect can reach C point directly from C point. Therefore, compared with other algorithms, quantum annealing algorithm may have better performance in some problems [14], [36].…”

Section: Quantum Annealing Algorithmmentioning

confidence: 99%

“…From the mathematical point of view, we deduced the quantum annealing algorithm completely in the previous work [14], and obtained the shortest TSP path and Hamiltonian quantity. In the following part, we use these to guide the subsequent quantum evolutionary algorithm.…”

Section: Introductionmentioning

confidence: 99%

Evolutionary Method of Sink Node Path Planning Guided by the Hamiltonian of Quantum Annealing Algorithm

Huang

Zhang

et al. 2021

IEEE Access

Self Cite

View full text Add to dashboard Cite

In order to solve the NP-hard problem of mobile sink path planning in wireless sensor networks (WSN) where the communication range is modeled as a circular area and overlaps with each other, this paper proposes a sink node path planning method guided by the Hamiltonian of quantum annealing algorithm (EMGH) to balance the energy consumption of wireless sensor networks, improve the network life and solve the energy hole problem. First of all, this paper analyzes the problem in theory, and transforms the characteristics of the problem into a mathematical model. The mathematical model considers that the sensor network itself is a travelling salesman problem (TSP), but also requires the shortest path of the mobile sink node. Then, the path of each node in the sensor network is iterated by quantum annealing algorithm, and an optimal TSP path is obtained by using quantum tunneling effect and quantum circuit to achieve parallelism. Finally, in the case of compressing the solution space, the moving path of the mobile sink node is quantum coded, and the Hamiltonian in the quantum annealing algorithm is taken as the guiding factor, and then the individual dimensions are moved, which improves the accuracy of the algorithm and speeds up the convergence speed of the algorithm. Finally, the feasibility of EMGH is verified by simulation experiments and comparison with other algorithms, which provides a reference for the optimization and improvement of path planning method.

show abstract

An Improved Noise Quantum Annealing Method for TSP

Cited by 9 publications

References 43 publications

Implementation of Quantum Annealing: A Systematic Review

Implementation of Quantum Annealing: A Systematic Review

Development of Econophysics: A Biased Account and Perspective from Kolkata

Evolutionary Method of Sink Node Path Planning Guided by the Hamiltonian of Quantum Annealing Algorithm

Contact Info

Product

Resources

About