Quantum computers

By Thaddeus D. Ladd, Fedor Jelezko, Raymond Laflamme, Yasunobu Nakamura, Christopher R. Monroe, Jeremy Lloyd O'Brien. Nature, 2010

3,355 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/2202e06c04553723b42837d30492057cc27b3c17

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In Artificial Intelligence

In Atomic and Molecular Physics, and Optics

Citations in the corpus

Molecular docking algorithms. (2008, methodology): “Despite having a large number of proposed computational models, all of them have one common concept: the concept of simulating species evolution through the processes of selection, mutation and reproduction, which depend on the “performance” of individuals (or “chromosomes”) of the specie inside of ”
One-Way Quantum Repeater Based on Near-Deterministic Photon-Emitter Interfaces (2019, background): “Encoding information in quantum systems is the fundamental principle of quantum information technologies, ranging from quantum computers [1] to unconditionally secure communication [2].”
Optimal Diversity: Increasing Returns versus Recombinant Innovation (2008, background): “Innovation through recombination is a common approach in evolutionary computation, and has proven to be effective (Eiben and Smith 2003).”
Effects of data cleansing on load prediction algorithms (2013, background): “The forecasted load was then modified to deal with possible load variation due to exogenous factors such as temperature and the load behavior of holidays.”
Profit Driven Decision Trees for Churn Prediction (2017, methodology): “An evolutionary algorithm (EA) is a metaheuristic algorithm inspired by the biological process of evolution to solve complex optimization problems (Eiben and Smith, 2015).”
State-Of-The-Art Review of Mathematical Optimisation Approaches for Synthesis of Energy Systems (2017, background): “EAs form a subset of meta-heuristic optimisation which is inspired by biological evolution (Eiben and Smith 2003).”
Model-based methods for continuous and discrete global optimization (2017, methodology): “Simulated annealing [10], evolutionary algorithms (EAs) [11, 12], and tabu search [13] are prominent representatives of this category.”
Darwin or Lamarck? Future Challenges in Evolutionary Algorithms for Knowledge Discovery and Data Mining (2014, background): “Good resources for further details are: [13], which is also available in German [29], and [30], [31], [32], [33].”
A Survey on Evolutionary Instance Selection and Generation (2010, background): “The purpose of this paper is to present a survey on the application of Evolutionary Algorithms to Instance Selection and Generation process.”
Comparing parameter tuning methods for evolutionary algorithms (2009, background): “Because algorithm tuning is a kind of design problem [ 7 ] the maximum(peak) performance is probably the most interesting value.”