scholarly journals Approximation Method for Optimization Problems in Gate-Model Quantum Computers

2021 ◽  
pp. 100066
Author(s):  
Laszlo Gyongyosi
Keyword(s):  
Approximation Method ◽  
Optimization Problems ◽  
Quantum Computers

SIMULATED ANNEALING SCHEMES IN TRANSIENTLY CHAOTIC NEURAL NETWORK MODEL

2004 ◽  
Vol 18 (17n19) ◽  
pp. 2579-2584 ◽  
Author(s):  
Y. C. FENG ◽  
X. CAI
Keyword(s):  
Neural Network ◽  
Simulated Annealing ◽  
Combinatorial Optimization ◽  
Neural Network Model ◽  
Approximation Method ◽  
Optimization Problems ◽  
Chaotic Neural Network ◽  
Combinatorial Optimization Problems ◽  
Search Speed ◽  
Evolution Function

A transiently chaotic neural network (TCNN) is an approximation method for combinatorial optimization problems. The evolution function of self-back connect weight, called annealing function, influences the accurate and search speed of TCNN model. This paper analyzes two common annealing schemes. Furthermore we proposed a new subsection exponential annealing function. Finally, we compared these annealing schemes in TSP problem.

scholarly journals Applying Quantum Optimization Algorithms for Linear Programming

2017 ◽  
Author(s):  
Mert Side ◽  
Volkan Erol
Keyword(s):  
Resource Allocation ◽  
Linear Programming ◽  
Production Scheduling ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Quantum Algorithms ◽  
Quantum Algorithm ◽  
Quantum Computers ◽  
Quantum Optimization ◽  
Special Case

Quantum computers are machines that are designed to use quantum mechanics in order to improve upon classical computers by running quantum algorithms. One of the main applications of quantum computing is solving optimization problems. For addressing optimization problems we can use linear programming. Linear programming is a method to obtain the best possible outcome in a special case of mathematical programming. Application areas of this problem consist of resource allocation, production scheduling, parameter estimation, etc. In our study, we looked at the duality of resource allocation problems. First, we chose a real world optimization problem and looked at its solution with linear programming. Then, we restudied this problem with a quantum algorithm in order to understand whether if there is a speedup of the solution. The improvement in computation is analysed and some interesting results are reported.

The Local Approximation Method for Structural Optimization

2014 ◽  
Vol 575 ◽  
pp. 854-858
Author(s):  
Yi Nie ◽  
Yan Wang ◽  
Wei Sun ◽  
Yan He ◽  
Jing Hao ◽  
...  
Keyword(s):  
Structural Optimization ◽  
Approximation Method ◽  
Optimization Problems ◽  
Broad Class ◽  
Local Approximation ◽  
Optimized Design ◽  
Guide Rail ◽  
Original Design ◽  
Design Variables

The local approximation method exhibits many advantage features and it is popular to a broad class of structural optimization problems. In this paper, both the mathematical modeling and case study of the local approximation method were studied. The theoretical analysis indicates that the primary optimization problem can be replaced with a sequence of explicit approximate problems by using the local approximation method. The explicit subproblems are convex and separable, which can be solved efficiently by using a dual method approach. The topology optimization of a guide rail design is then solved to testify the proposed method, which has been coded by Altair OptiStruct. The optimized design of a widely used guide rail with an “I” shape cross section is obtained and compared with the original design. The numerical results have shown that the local approximation method can effectively solve the structure optimization problems, especially the ones with hundreds of design variables or constraints.

scholarly journals Feature Selection for Recommender Systems with Quantum Computing

Entropy ◽  
2021 ◽  
Vol 23 (8) ◽  
pp. 970
Author(s):  
Riccardo Nembrini ◽  
Maurizio Ferrari Dacrema ◽  
Paolo Cremonesi
Keyword(s):  
Feature Selection ◽  
Quantum Computing ◽  
Recommender Systems ◽  
Domain Knowledge ◽  
Optimization Problems ◽  
Quantum Computer ◽  
Mathematical Formulation ◽  
Easy Access ◽  
Quantum Computers ◽  
User Interactions

The promise of quantum computing to open new unexplored possibilities in several scientific fields has been long discussed, but until recently the lack of a functional quantum computer has confined this discussion mostly to theoretical algorithmic papers. It was only in the last few years that small but functional quantum computers have become available to the broader research community. One paradigm in particular,quantum annealing, can be used to sample optimal solutions for a number of NP-hard optimization problems represented with classical operations research tools, providing an easy access to the potential of this emerging technology. One of the tasks that most naturally fits in this mathematical formulation is feature selection. In this paper, we investigate how to design a hybrid feature selection algorithm for recommender systems that leverages the domain knowledge and behavior hidden in the user interactions data. We represent the feature selection as an optimization problem and solve it on a real quantum computer, provided by D-Wave. The results indicate that the proposed approach is effective in selecting a limited set of important features and that quantum computers are becoming powerful enough to enter the wider realm of applied science.

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