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TOWARDS A QUANTUM APPROACH TO CELL MEMBRANE
TOWARDS A QUANTUM APPROACH TO CELL MEMBRANE

The Quantum Century
The Quantum Century

MLMAT 2017 Special Session on Machine Learning
MLMAT 2017 Special Session on Machine Learning

... Artificial Intelligence (AI) has been applied successfully to many fields such as data analysis, finance, multimedia, signal and image processing, web technologies, robotics, and automations, etc. Machine learning, as a major technology behind AI, is changing the world rapidly by deploying varied al ...
Towards a quantum approach to cell membane electrodynamics
Towards a quantum approach to cell membane electrodynamics

... from this operation, therefore rests upon these “non-classical” processes. Two local mechanisms, a depolarization of the membrane and the opening of the channels, on the other hand the permanent output of the K+ ion of the cell at rest, and the “return” of the Na+ and K+ ions at the moment of the re ...
Presentation453.22
Presentation453.22

Quantum emission dynamics from a single quantum dot in a planar
Quantum emission dynamics from a single quantum dot in a planar

... above for PPC nanocavitiies with Q = 1000, 5000, 15,000. In Fig. 2, left, the upper decay level is shown for the QD within the nanocavity; for reference the homogeneous material solution is also shown. Clear vacuum Rabi oscillations are obtained for an increasing Q, even for the modest Q values that ...
Document
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... different approaches to approximate the matrix exponential e-βH. In the approach presented in this paper, we represent e-βH as the Green’s function of the semigroup generated by the exponential. The logarithmic Bloch equation is now solved asymptotically, using the Born approximation, i.e. by iterat ...
Quantum Information and Randomness - Max-Planck
Quantum Information and Randomness - Max-Planck

Part I - TTU Physics
Part I - TTU Physics

... • Reif, as all modern texts, takes the viewpoint that the system’s “State” is described by a 2fdimensional phase space ...
Quantum Optics and Quantum Information with - GdR-IQFA
Quantum Optics and Quantum Information with - GdR-IQFA

LOCALLY NONCOMMUTATIVE SPACETIMES JAKOB G. HELLER, NIKOLAI NEUMAIER AND STEFAN WALDMANN
LOCALLY NONCOMMUTATIVE SPACETIMES JAKOB G. HELLER, NIKOLAI NEUMAIER AND STEFAN WALDMANN

Physics 214 Lecture 11
Physics 214 Lecture 11

Spacetime structures of continuous
Spacetime structures of continuous

6 GU 2007 Quantum Illusions and Time
6 GU 2007 Quantum Illusions and Time

...  Rn t ...
Lecture 5
Lecture 5

... only O(n) times, performs only O(n 3) auxiliary operations (for the Hadamards, measurements, and linear algebra), and succeeds with probability ¾ ...
Quantum phase transitions in Kitaev spin models
Quantum phase transitions in Kitaev spin models

Parallel algorithms for 3D Reconstruction of Asymmetric
Parallel algorithms for 3D Reconstruction of Asymmetric

Introduction to Quantum Statistics
Introduction to Quantum Statistics

... probabilistic predictions of the behavior of ensembles (ideally an infinite number of identically prepared quantum systems) using density operators. This description was completely sufficient for the first 60 years of the existence of quantum mechanics because it was generally regarded as completely ...
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pdf

Quantum Mechanics
Quantum Mechanics

A translation of" A New Solution to the Measurement Problem of
A translation of" A New Solution to the Measurement Problem of

Here
Here

Department of Mathematics Research Colloquia 2001 - 2002 Prof Tim Gowers Friday
Department of Mathematics Research Colloquia 2001 - 2002 Prof Tim Gowers Friday

Quantum Computing
Quantum Computing

here. - psychicQuesting.com
here. - psychicQuesting.com

< 1 ... 146 147 148 149 150 151 152 153 154 ... 212 >

Quantum machine learning

Quantum machine learning is a newly emerging interdisciplinary research area between quantum physics and computer science that summarises efforts to combine quantum mechanics with methods of machine learning. Quantum machine learning models or algorithms intend to use the advantages of quantum information in order to improve classical methods of machine learning, for example by developing efficient implementations of expensive classical algorithms on a quantum computer. However, quantum machine learning also includes the vice versa approach, namely applying classical methods of machine learning to quantum information theory.Although yet in its infancy, quantum machine learning is met with high expectations of providing a solution for big data analysis using the ‘parallel’ power of quantum computation. This trend is underlined by recent investments of companies such as Google and Microsoft into quantum computing hardware and research. However, quantum machine learning is still in its infancy and requires more theoretical foundations as well as solid scientific results in order to mature to a full academic discipline.
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