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the computational complexity of noncommutative graphs.
the computational complexity of noncommutative graphs.

Document
Document

... T=TKT T=2/3 TKT ...
Quantum Physics 2005
Quantum Physics 2005

... • This principle states that you cannot know both the position and momentum of a particle simultaneously to arbitrary accuracy. – There are many approaches to this idea. Here are two. • The act of measuring position requires that the particle intact with a probe, which imparts momentum to the partic ...
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E nergy spectra of quantum rings

1 Universal entanglement dynamics Quantum Entanglement Growth
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Verification of Concurrent Quantum Protocols by Equivalence
Verification of Concurrent Quantum Protocols by Equivalence

... not everyone is convinced that it is truly quantum. On the other hand, quantum communication and cryptography have made large strides and is now well established. Physical restrictions of quantum communication, like preserving photon states over long distances, are gradually being resolved, for exa ...
Are there basic laws of quantum information processing?
Are there basic laws of quantum information processing?

Beables for Quantum Electrodynamics
Beables for Quantum Electrodynamics

Solid-state quantum computing using spectral holes M. S. Shahriar, P. R. Hemmer,
Solid-state quantum computing using spectral holes M. S. Shahriar, P. R. Hemmer,

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Pulsed Energy-Time Entangled Twin

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... Section 2.5∗ :Quadratic Effects in the E × e-Problem We conclude our outline of the E×e - problem by pointing out that it contains far more points of degeneracy than just the origin, ρ = 0, and is thus of considerably higher complexity than suggested by the foregoing discussion. Expanding the potenti ...
Negative Quasi-Probability, Contextuality, Quantum Magic and the
Negative Quasi-Probability, Contextuality, Quantum Magic and the

Dirac`s coincidences sixty years on
Dirac`s coincidences sixty years on

quantum brownian motion and the third law of thermodynamics
quantum brownian motion and the third law of thermodynamics

... extensive and never decreases for a closed physical system. In addition, the second law tells us that there exists an absolute zero of temperature. The Third Law is attributed to Walther Hermann Nernst (1864–1941) and arose as the result of his seminal idea — being guided by his critical analysis of ...
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ppt - Harvard Condensed Matter Theory group

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6 Theory of the topological Anderson insulator

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CSE 599d - Quantum Computing Introduction and Basics of

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Quantum analogue computing

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A Short History of the Interaction Between QFT and Topology

Quantum Computation with Topological Phases of Matter
Quantum Computation with Topological Phases of Matter

Nicholas Bigelow - University of Rochester
Nicholas Bigelow - University of Rochester

... variables (quadrature phases) has been demonstrated. Continuous variables are advantageous because they provide access to an infinite dimensional state space. It is hard to “store” light ...
D-Wave quantum computer
D-Wave quantum computer

... This approach was first suggested by R. Feynman in 1982[1] stating that quantum computers would simulate much better quantum systems than classical computers. However, there was not a big movement in the field until 1994 when Peter Shor [2] showed that an algorithm exists to factorize numbers whose ...
Quantum Computing: The Risk to Existing Encryption Methods
Quantum Computing: The Risk to Existing Encryption Methods

PPT - Fernando Brandao
PPT - Fernando Brandao

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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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