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What is Quantum Annealing

Handbook of Research on Natural Computing for Optimization Problems
This technique utilizes “fluctuations in the protein models by following quantum mechanics” as a substitute for heat fluctuations or transitions to prevail over elevated but slight obstructions in the target function.
Published in Chapter:
Adaptive Simulated Annealing Algorithm to Solve Bio-Molecular Optimization
Sujay Ray (University of Kalyani, India)
DOI: 10.4018/978-1-5225-0058-2.ch020
Abstract
Energy minimization is a paramount zone in the field of computational and structural biology for protein modeling. It helps in mending distorted geometries in the folded functional protein by moving its atoms to release internal constraints. It attempts to hold back to zero value for the net atomic force on every atom. But to overcome certain disadvantages in energy minimization, Simulated Annealing (SA) can be helpful. SA is a molecular dynamics technique, where temperature is gradually reduced during the simulation. It provides the best configuration of bio-molecules in shorter time. With the advancement in computational knowledge, one essential but less sensitive variant of SA: Adaptive Simulated Annealing (ASA) algorithm is beneficial, because it automatically adjusts the temperature scheme and abrupt opting of step. Therefore it benefits to prepare stable protein models and further to investigate protein-protein interactions. Thus, a residue-level study can be analyzed in details for the benefit of the entire biota.
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An Introductory Study to Quantum Programming
Quantum annealing is the process of finding the minimum energy state of something where instead of focusing on trying to find the minimum energy state, a sample from any low energy state is taken and try and characterize the shape of the energy landscape. This is useful for applications like machine learning where we try to build a probabilistic representation of the world and these samples give us information about what the model looks like now and these models can be used over time.
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Next Generation of Intelligent Cities: Case Studies From Europe
Quantum annealing is used to optimize discrete binary optimization problems by finding the global minima of a given function. It is useful in solving problems where there are large numbers of local minima present. D-Wave Systems introduced the first quantum annealer in 2011, named D-Wave One.
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Data Science for Industry 4.0
Solving complex optimization problems using quantum fluctuations.
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