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Simulation of Quantum Computation with Wolfram
Simulation of Quantum Computation with Wolfram

... Quantum computation and quantum information is a rapidly developing research area of modern science and technology. Quantum computers are to be able to perform certain computational tasks much more efficiently than classical computers. At the same time a realistic quantum computer is still not availab ...
学术报告
学术报告

... quantum phase transitions based on its leading term, i.e. the fidelity susceptibility. The fidelity susceptibility denotes the adiabatic leading response of the ground state to the driving parameter. Differ from traditionally approach based on the ground-state energy, the fidelity susceptibility sho ...
4_POSER_FAEN
4_POSER_FAEN

The Learnability of Quantum States
The Learnability of Quantum States

... Our Basic Result: Suppose there were a polynomialtime classical randomized algorithm that took as input a description of a quantum optics experiment, and output a sample from the correct final distribution over n-photon states. Then the polynomial hierarchy would collapse. Motivation: Compared to (s ...
Thermal de Broglie Wavelength
Thermal de Broglie Wavelength

Mott insulators, Noise correlations and Coherent Spin Dynamics in Optical Lattices
Mott insulators, Noise correlations and Coherent Spin Dynamics in Optical Lattices

... Similar to Richard Feynman’s original proposal for a quantum computer as a simulator for the quantum dynamics of other physical systems, neutral atoms in optical lattices already today offer powerful possibilities for simulating fundamental Hamiltonians of condensed matter physics. In fact, many nov ...
Answer
Answer

class (Recovered)
class (Recovered)

Abstract
Abstract

Detection of entanglement and of features of quantum evolution with
Detection of entanglement and of features of quantum evolution with

... systems are called complementary if they are such that, if one knows the value of one property, all possible values of the other property are equiprobable. We will first provide an interpretation of entanglement in composite systems based on classical correlations between measurement outcomes of com ...
QUANTUM DOTS
QUANTUM DOTS

... quantum dots rather than by spectroscopic manipulation as in other models. The tunnel barrier between dots can be high or low by setting a gate voltage. In the case of the high barrier potential the tunnelling is forbidden between dots (no evolution in time). In the low barrier potential spins will ...
3.4 Quantum Numbers
3.4 Quantum Numbers

Holonomic quantum computation with neutral atoms
Holonomic quantum computation with neutral atoms

... [1] is a dynamical one: in order to manipulate the quantum state of systems encoding information, local interactions between low dimensional subsystems (qubits) are switched on and off in such a way to enact a sequence of quantum gates. On the other hand, ever since the discovery of the Berry’s phase ...
II. Units of Measurement
II. Units of Measurement

... Heisenberg’s Uncertainty Principle There is a limit to just how precisely we can know both the position and velocity of a particle at a given time. ...
Baby-Quiz
Baby-Quiz

... 2. Suppose you were a nineteenth-century scientist who had just discovered a new phenomenon known as Zeta rays. What experiment could you perform to define if Zeta rays are charged particles or e/m waves? Could this experiment distinguish between neutral particles and an e/m wave? 3. If a metal surf ...
Nonlinearity in Classical and Quantum Physics
Nonlinearity in Classical and Quantum Physics

Inverse mapping
Inverse mapping

Letná škola z fyziky vysokých energií, Svit, 9
Letná škola z fyziky vysokých energií, Svit, 9

Light-Matter-Interaction in Nanostructures: Semi
Light-Matter-Interaction in Nanostructures: Semi

Quantum Numbers
Quantum Numbers

Course Poster
Course Poster

Quantum Problems 1. Consider a quantum system whose state at
Quantum Problems 1. Consider a quantum system whose state at

... 1. Consider a quantum system whose state at time t1 is given by |Ψ(t1 )i = √12 (|ψ1 i + |ψ2 i), where |ψ1 i and |ψ2 i are energy eigenstates with eigenvalues E1 and E2 respectively. (E1 6= E2 .) (a) Calculate the uncertainty ∆E of the system, as well as the first time t2 > t1 at which |Ψ(t2 )i becom ...
Special Issue on Lie Group Representation Theory, Coherent States,
Special Issue on Lie Group Representation Theory, Coherent States,

... is an essential resource when facing those two fundamental problems, either as a valuable (mathematical) classification tool, a (gauge) guiding principle, or an essential building block, when the group structure is taken to the ultimate consequences. Although the representation theory of finitedimen ...
Possible Topics for the Final Project Taken with slight modification
Possible Topics for the Final Project Taken with slight modification

... 17. Supersymmetric quantum mechanics. 18. The Zeeman effect in weak, intermediate and strong magnetic fields. 19. The Lamb shift in hydrogen — evidence that relativistic quantum mechanics must be replaced by quantum field theory. 20. The non-relativistic quark model of the proton, neutron and relate ...
Quantum Computing at the Speed of Light
Quantum Computing at the Speed of Light

... deterministic control of distant, solid state qubits encoded in excitons in semiconductor quantum dots [1-3]. In these experiments, we have employed a technique called optimal quantum control (OQC), in which one tailors the phase and amplitude of the control Hamiltonian through femtosecond pulse sha ...
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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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