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Plausible Explanation of Quantization of Intrinsic Redshift from Hall
Plausible Explanation of Quantization of Intrinsic Redshift from Hall

PHYSICS 673 Nonlinear and Quantum Optics
PHYSICS 673 Nonlinear and Quantum Optics

... When we study fields described by the slowly varying envelope approximation, we are often only interested in learning about the evolution of the envelope rather than the carrier. If this is the case, we can significantly simplify our math. Before we proceed to calculate this evolution, let us note tha ...
Get PDF - OSA Publishing
Get PDF - OSA Publishing

Classical and Quantum Algorithms for Finding Cycles
Classical and Quantum Algorithms for Finding Cycles

Biological Autonomy
Biological Autonomy

Modeling quantum fluid dynamics at nonzero temperatures
Modeling quantum fluid dynamics at nonzero temperatures

... ab initio modeling (29), with their finite size, high compressibility, and lower disparity between intervortex distances and core sizes (compared with superfluid helium) leading to the probing of a very different turbulent regime (30). Despite the large body of research on superfluid turbulence, num ...
9 Quantum Phases and Phase Transitions of Mott
9 Quantum Phases and Phase Transitions of Mott

... each unit cell. A simple approach that can be used for a theoretical description of such insulators is the method of bond operators [14, 15]. This method has been widely applied, and in some cases provides an accurate quantitative description of numerical studies and experiments [10, 16]. We will de ...
Hidden Variables and Nonlocality in Quantum Mechanics
Hidden Variables and Nonlocality in Quantum Mechanics

Studies of Infinite Two-Dimensional Quantum Lattice
Studies of Infinite Two-Dimensional Quantum Lattice

... Determining the properties of quantum many-body systems is a central challenge in modern physics. Being able to determine the macroscopic properties of a system from its microscopic description would hasten progress in many fields of science and technology. However, we currently lack the tools to so ...
Quantum Information with Fermionic Gaussian States - Max
Quantum Information with Fermionic Gaussian States - Max

... In physics, Gaussian approximation is a frequently used tool for solving many-body problems. Gaussian approximation relies on describing system fully in terms of two-point correlation functions. This means that all the higher order correlations can be expressed as a product of two-point correlation ...
Full-Text PDF
Full-Text PDF

Imaging Electrons in Few-Electron Quantum Dots
Imaging Electrons in Few-Electron Quantum Dots

Quantum fluctuations in modulated nonlinear oscillators Vittorio Peano and M I Dykman
Quantum fluctuations in modulated nonlinear oscillators Vittorio Peano and M I Dykman

MOCK MODULAR FORMS AND QUANTUM MODULAR FORMS 1
MOCK MODULAR FORMS AND QUANTUM MODULAR FORMS 1

... This map Qf is well-defined by Theorem 1.1.(3) because for each rational number x there is a unique qi equivalent to x under the action of Γ0 (N ) (see section 3 for details). Motivated by a number of examples from quantum invariants of 3-manifolds, Vassiliev invariants of knots, and period function ...
Paper - Quantum Electrodynamics in Graphene
Paper - Quantum Electrodynamics in Graphene

Universitat Autonoma de Barcelona Facultat de Ciencies, Departament de F sica
Universitat Autonoma de Barcelona Facultat de Ciencies, Departament de F sica

... which mathematically correspond to elements of a two-dimensional Hilbert space, and can be expressed as a superposition of two states, namely j0i and j1i. Thus, the most general states of quantum information are superpositions of strings of qubits. Physically, a qubit corresponds to a single quantum ...
Diamond Photonics
Diamond Photonics

Wavefunctions and carrier-carrier interactions in InAs quantum dots
Wavefunctions and carrier-carrier interactions in InAs quantum dots

Title Visible to near infrared conversion in Ce3+-Yb3+ Co
Title Visible to near infrared conversion in Ce3+-Yb3+ Co

the hydrogen atom in a uniform magnetic field - an example
the hydrogen atom in a uniform magnetic field - an example

... Although the equations of motion of classical mechanics are strictly deterministic, the actual path along which a complex classical system evolves may depend extremely sensitively on the initial conditions so that the evolution of the system becomes de facto unpredictable. Such a system is said to b ...
- Quantum Optics and Spectroscopy
- Quantum Optics and Spectroscopy

Time dependent entanglement features, and other quantum information aspects,
Time dependent entanglement features, and other quantum information aspects,

LOCAL UNITARY REPRESENTATIONS OF THE
LOCAL UNITARY REPRESENTATIONS OF THE

... Topological quantum computation (TQC) is based on the storage and manipulation of information in the representation spaces of the braid group, which consist of quantum states of certain topological phases of matter [22]. The most important unitary braid group representations for TQC are the Jones re ...
Introduction to Quantum Computation THE JOY OF ENTANGLEMENT
Introduction to Quantum Computation THE JOY OF ENTANGLEMENT

... mechanics is incomplete, as EPR claimed. These hidden variables are local because their local interaction with a measuring device determines the measurement result. To be explicit, let us assume that the particles reach Alice and Bob with prepared answers to the questions that Alice and Bob ask. The ...
AO04703247251
AO04703247251

... Nidhi Int. Journal of Engineering Research and Applications ISSN : 2248-9622, Vol. 4, Issue 7( Version 3), July 2014, pp.247-251 The proposed reversible ripple carry adder and carry skip adder circuits are more efficient than the existing circuits presented in [7] and [10]. Evaluation of proposed c ...
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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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