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An Efficient Learning Procedure for Deep Boltzmann Machines
An Efficient Learning Procedure for Deep Boltzmann Machines

... much too slow to be practical for learning large, multilayer Boltzmann machines. Even for small networks, the learning rate must be very small to avoid an unexpected effect: the high variance in the difference of the two estimated statistics has a tendency to drive the parameters to regions where ea ...
memory effects in the dynamics of open quantum systems
memory effects in the dynamics of open quantum systems

... systems have been extensively studied in the last decades and various analytical methods and numerical simulation techniques have been developed in order to give insights into the nature of non-Markovian effects [6–8]. ...
Shor`s Algorithm and Factoring: Don`t Throw Away the Odd Orders
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QUANTUM GROUPS AND DIFFERENTIAL FORMS Contents 1
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... The coaction of Mq on the generators of Ω(Aq ) is given by equation (1.1). In principle, the theorem can be verified directly from the definitions. But that is not a good approach because it does not tell us how to construct Ω(Aq ) and Mq in the first place. We now address this question. 1.7. Method ...
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Quantum Information Chapter 10. Quantum Shannon Theory
Quantum Information Chapter 10. Quantum Shannon Theory

... Ω(1) denotes a positive constant. This is Shannon’s source coding theorem. We have not discussed at all the details of the compression code. We might imagine a huge lookup table which assigns a unique codeword to each message and vice versa, but because such a table has size exponential in n it is q ...
Inconsistencies of the Adiabatic Theorem and the Berry Phase
Inconsistencies of the Adiabatic Theorem and the Berry Phase

Minimal normal measurement models of quantum instruments
Minimal normal measurement models of quantum instruments

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... My version of Quantum Shannon Theory is no substitute for the more careful treatment in Wilde’s book [1], but it may be more suitable for beginners. This chapter contains occasional references to earlier chapters in my book, but I hope it will be intelligible when read independently of other chapter ...
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Optical Properties of Semiconductor Quantum Dots

... called) Matlab course and the second year quantum mechanics course. It was really a pleasure to prepare the exercises, to discuss with the students and to explain them something. I am grateful to Fred Brok and to Lieven Vandersypen for letting me contribute to their courses. Samir, Stevan, it was fu ...
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Public Keys and Private Keys Quantum Cryptography

... Quantum private keys. In this part we examine the possibility to design quantum key distribution protocols with the same simplicity, but with improved level of security, with respect to today’s common single photon protocols, such as BB84 and the six-state protocol [16, 22, 7]. In particular, we se ...
Optimized Reversible Vedic Multipliers for High Speed Low Power
Optimized Reversible Vedic Multipliers for High Speed Low Power

... A. Literature Survey and Significance of reversible logic Conventional combinational logic circuits are known to dissipate heat for every bit of information that is lost. This is also evident from the second law of thermodynamics which states that any irreversible process leads to loss of energy. La ...
Entanglement and Quantum Cryptography
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... applications. The present thesis covers several topics on quantum cryptography, such as the security analysis of quantum channels for key distribution protocols and the study of quantum cloning. First, we introduce a general formalism to characterize the cryptographic properties of quantum channels ...
Interaction-based nonlinear quantum metrology with a cold atomic ensemble
Interaction-based nonlinear quantum metrology with a cold atomic ensemble

... In the context of quantum physics, quantum metrology develops high-resolution and highly sensitive measurements of parameters using quantum theory to describe the physical systems, and in particular exploiting quantum entanglement. The declared aim of quantum metrology is to develop new measurement ...
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... applications. The present thesis covers several topics on quantum cryptography, such as the security analysis of quantum channels for key distribution protocols and the study of quantum cloning. First, we introduce a general formalism to characterize the cryptographic properties of quantum channels ...
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The Puzzling Story of the Neutral Kaon System or what we can learn

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Single defect centres in diamond: A review

... Fig. 1 (online colour at: www.pss-a.com) Schematic representation of the nitrogen vacancy (NV) centre structure. ...
Quantum Information Processing with Finite Resources
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... grasp the experimental consequences qualitatively and see that the theory does not lead to any contradictions. Heisenberg, 1927, p. 172) His goal was, of course, to show that, in this new sense of the word, matrix mechanics could lay the same claim to Anschaulichkeit as wave mechanics. To do this, h ...
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[235] JPhysConfSer_702(2016)012001

... the consequent existence of strong quantum fluctuations among the states in the degenerate manifold. Either quantum or thermal fluctuations can then, in such a situation, suppress magnetic LRO, and the possibility of such exotic non-classical states as those discussed above forming the stable GS pha ...
Two-resonator circuit quantum electrodynamics: Dissipative theory
Two-resonator circuit quantum electrodynamics: Dissipative theory

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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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