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Quantum Geometry: a reunion of Physics and Math
Quantum Geometry: a reunion of Physics and Math

... A space X with a non-commutative rule for multiplying functions on X is an example of a quantum space (or non-commutative space). Quantum geometry is the study of such “spaces”. The goal of Deformation Quantization is to turn a Poisson space (a space with a Poisson bracket) into a non-commutative s ...
PPT
PPT

... • The central problem of the Folk approach is that it does not specify at what point the linear wave eq ceases to apply, and thus is incomplete in the sense that it does not fully describe whether interference effects will be found in hypothetical experiments with large-scale quantum coherence. • Th ...
Interaction between quantum dots and superconducting microwave resonators Tobias Frey
Interaction between quantum dots and superconducting microwave resonators Tobias Frey

... Interconnect the worlds of semiconductor and superconductor based quantum circuits Circuit quantum electrodynamics ...
Chapter 2: Interacting Rydberg atoms
Chapter 2: Interacting Rydberg atoms

Theory and simulations of quantum glass forming liquids
Theory and simulations of quantum glass forming liquids

... coexist.6,7 In such cases, which range from the behavior of superfluid helium under high pressure to the phase diagram of quantum random optimization problems, the interplay between quantum mechanics and the otherwise classical fluctuations that lead to vitrification can be expected to produce quali ...
PPT
PPT

... The spin relaxation depends on THREE major factors: 1. Moving velocity, reflected by Doppler effect 2. Magnetic field, determining the original Zeeman splitting 3. Quantum confinement, causing the phonon bottleneck effect ...
Machine learning and Neural Networks
Machine learning and Neural Networks

... • Boolean logic and resolution • Evolutionary machine learning – many algorithms / neural networks are generated to solve a problem, the best ones survive • Statistical learning • Unsupervised learning – algorithm that models outputs from the input, knows nothing about the expected results • Supervi ...
Photon Wave Mechanics: A De Broglie-Bohm Approach
Photon Wave Mechanics: A De Broglie-Bohm Approach

... All the known experiments show that the probabilistic interpretation of the wave function is indeed the correct one (see any textbook on quantum mechanics, for example [3]), and here we do not question that. However, experimental results do not force us, as well, to consider the wave function S as " ...
Time of the Energy Emission in the Hydrogen Atom and Its
Time of the Energy Emission in the Hydrogen Atom and Its

Thermal effects on sudden changes and freezing
Thermal effects on sudden changes and freezing

Sympo. lV-10 Spin-Splitting Reversal in InGaAs,4nP Quantum
Sympo. lV-10 Spin-Splitting Reversal in InGaAs,4nP Quantum

... InGaAs cap layer, InP barrier, and the InGaAs quantum well. Finally the etched wires were embedded in InP by MOCVD overgrowth. The wires used the experiments span a lateral wiOtn from 450 A to 250 A. We define the growth direction to be the z-direction, the wire axis to the y-direction and the later ...
fundamental_reality\holographic paradigm\morphogenetic fields
fundamental_reality\holographic paradigm\morphogenetic fields

... is carrying form or information from all around. It doesn't, within its limits, depend on how strong the radio wave is. So we could say that in that sense the quantum potential is acting as a formative field on the movement of the electrons. The formative field could not be put in three-dimensional ...
On Quantum Versions of Record
On Quantum Versions of Record

... number of steps. Then TQ = t0 + t1 + . . . + ts , where t0 denotes the number of non-quantum steps in AQ , s denotes the number of Grover’s searches, and ti denotes the time required for i-th quantum search. To show that the first statement holds, let us recall that the Grover’s algorithm searches t ...
Topological Phases of Matter classification and application
Topological Phases of Matter classification and application

PPT - LSU Physics & Astronomy
PPT - LSU Physics & Astronomy

Abstract: - QCCQI 2008
Abstract: - QCCQI 2008

the heisenberg uncertainty relation derived by multiplying matter
the heisenberg uncertainty relation derived by multiplying matter

--Fundamental Problems and Application to Material Science-
--Fundamental Problems and Application to Material Science-

A violation of the uncertainty principle implies a violation of the
A violation of the uncertainty principle implies a violation of the

(pdf)
(pdf)

Matter–wave interference of particles selected from a molecular
Matter–wave interference of particles selected from a molecular

Document
Document

... No two electrons can have the same set of quantum numbers. That is, no two electrons can be in the same quantum state. From the exclusion principle, it can be seen that only two electrons can be present in any orbital: One electron will have spin up and one spin down. Maximum number of electrons in ...
PDF
PDF

... such as red, blue, green, yellow, black, grey, or clear Legos. But, once decided, the laser obeys a conservation law that means that the color of the Lego blocks won’t change in time. The constancy of the color represents the invariance. Once the invariance is in place, one can make any state of a l ...
Macroscopicity of Mechanical Quantum Superposition States
Macroscopicity of Mechanical Quantum Superposition States

Properties, Statistics and the Identity of Quantum Particles
Properties, Statistics and the Identity of Quantum Particles

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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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