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Programmable architecture for quantum computing Jialin Chen, Lingli Wang, Edoardo Charbon,
Programmable architecture for quantum computing Jialin Chen, Lingli Wang, Edoardo Charbon,

... the QFPGA which consists of four parts. The first two parts are the structures of the QRC and QLB, the third part is the error analysis, and the fourth part describes the whole architecture of QFPGA. Then in Sec. VI, based on QFPGA, we provide two applications—the general quantum gates and quantum F ...
M10/17
M10/17

Comment on “The quantum pigeonhole principle and the nature of
Comment on “The quantum pigeonhole principle and the nature of

On quantum obfuscation - University of Maryland Institute for
On quantum obfuscation - University of Maryland Institute for

... and Diffie and Hellman [17] in the 1970s. These public-key methods have found widespread practical application in virtually all Internet communications. More advanced theoretical methods for encrypting data, such as fully-homomorphic encryption, have only been discovered recently [22], but show grea ...
Quantum Query Algorithms - Baltic Journal of Modern Computing
Quantum Query Algorithms - Baltic Journal of Modern Computing

... for solving specific computational problems, as well as to improve the general construction techniques for algorithms. It is important to work out an approach for designing efficient quantum algorithms for arbitrary functions. A collection of existing methods (including, for instance, a method for e ...
PDF
PDF

Singularity of the time-energy uncertainty in adiabatic perturbation
Singularity of the time-energy uncertainty in adiabatic perturbation

... field. Its quantum trajectory is shown to be a cycloid on the Bloch sphere, traced by a point on a rolling circle, of a radius determined by the angular speed of the magnetic field, along the adiabatic path of the instantaneous eigenstate. We find the two basic geometric quantities, the distance and ...
Whole-Parts Strategies in Quantum Chemistry: Some Philosophical
Whole-Parts Strategies in Quantum Chemistry: Some Philosophical

Dyson equation for diffractive scattering
Dyson equation for diffractive scattering

Switching via quantum activation: A parametrically modulated oscillator 兲
Switching via quantum activation: A parametrically modulated oscillator 兲

Isotropic restriction in Group Field Theory condensates
Isotropic restriction in Group Field Theory condensates

... Figure 1.1: Construction of the physical Hilbert space of LQG. The kinematical Hilbert space Hkin is built using functions of holonomies on graphs embedded in Σ. We implement the three constraints one after another to obtain the physical Hilbert space Hphys . Kinematical Hilbert Space. The basic ele ...
Bounding the quantum dimension with contextuality Linköping University Post Print
Bounding the quantum dimension with contextuality Linköping University Post Print

Why Machine Learning? - Lehrstuhl für Informatik 2
Why Machine Learning? - Lehrstuhl für Informatik 2

... Artificial Intelligence:Learning: Learning symbolic representation of concepts, ML as search problem , Prior knowledge + training examples guide the learning-process Bayesian Methods:Calculating probabilities of the hypotheses, Bayesian-classifier Theory of the computational complexity: Theoretical ...
Quantum Channels - Institut Camille Jordan
Quantum Channels - Institut Camille Jordan

... Let us recall the setup of quantum open systems. We are given two quantum systems interacting together, with state space H and K respectively. Our approach is in discrete time only in this section, that is, we shall look at the evolution of the two systems together for a fixed time duration τ . This ...
The quantum mechanical tipping pencil--
The quantum mechanical tipping pencil--

... Online at stacks.iop.org/EJP/28/1097 Abstract ...
abstracts - Istituto Nazionale di Fisica Nucleare
abstracts - Istituto Nazionale di Fisica Nucleare

Finding shortest lattice vectors faster using quantum search
Finding shortest lattice vectors faster using quantum search

... fundamental reasons for questioning it [12], and there are practical computing architectures where the assumption does not apply. In the case of quantum computation, a practical RAM-like quantum memory (e.g. [37]) looks particularly challenging, especially for first generation quantum computers. Som ...
Path Integrals
Path Integrals

Quantum Money from Hidden Subspaces
Quantum Money from Hidden Subspaces

... the unusual mathematics employed, the work of Farhi et al. [22] (building on [30]) also developed an idea that will play a major role in our work. That idea is to construct public-key quantum money schemes by composing two “simpler” ingredients: first, objects that we call mini-schemes; and second, ...
Concentration-dependent absorption and emission behaviour of
Concentration-dependent absorption and emission behaviour of

Scientific discoveries limit our knowledge
Scientific discoveries limit our knowledge

Creation of entangled states in coupled quantum dots via adiabatic... C. Creatore, R. T. Brierley, R. T. Phillips,
Creation of entangled states in coupled quantum dots via adiabatic... C. Creatore, R. T. Brierley, R. T. Phillips,

... adiabatic rapid passage, for the creation of entangled states in an ensemble of pairwise coupled two-level systems, such as an ensemble of coupled quantum dots. We show by quantitative analysis using realistic parameters for semiconductor quantum dots that this method is feasible where other approac ...
Quantum mechanics: Myths and facts
Quantum mechanics: Myths and facts

Superconducting Circuits and Quantum Computation—T. P. Orlando
Superconducting Circuits and Quantum Computation—T. P. Orlando

Topos logic in measurement-based quantum computation
Topos logic in measurement-based quantum computation

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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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