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Influence of measurements on the statistics of work performed on a
Influence of measurements on the statistics of work performed on a

LAUDISA, Counterfactual reasoning, realism and QM_last version
LAUDISA, Counterfactual reasoning, realism and QM_last version

Preparation and measurement in quantum physics
Preparation and measurement in quantum physics

... single elements of the ensemble. Unfortunately, the latter notion became an institution, while the former rational analysis was ignored. In the aftermath of the Einstein(5)-Bohr (6) controversies, Margenau (7) became the first to point out the absurdity of the projection postulate and to emphasize t ...
One-way quantum computing with arbitrarily large time
One-way quantum computing with arbitrarily large time

... scalability breakthrough, optical implementations of quantum information offer other advantages such as room-temperature operation, naturally low decoherence, and significant potential for device integration [19,20]. In this paper, we show that one can create computationally universal CV cluster sta ...
Probability in the Many-Worlds Interpretation of Quantum Mechanics
Probability in the Many-Worlds Interpretation of Quantum Mechanics

... have at present, we can follow our branch to the past before the last quantum measurement, but we can follow it to the future only until the next measurement. In our branch we remember past events of performing quantum measurements and obtaining particular results. It seems to us that the outcomes c ...
view Pdf - Informatik - FB3
view Pdf - Informatik - FB3

... been considered as optimization objectives thus far. However, beyond that also the depth of a quantum circuit is vital. Although first synthesis approaches that consider depth have recently been introduced, the majority of design methods did not consider this metric. In this paper, we introduce an o ...
A Quantum Algorithm for Finding Minimum Exclusive
A Quantum Algorithm for Finding Minimum Exclusive

... Grover’s algorithm [2]. We propose a special oracle (QMinOracle) as input for Grover’s algorithm, which is based on Theorem 2. In a conventional ESCT (or ESOP) minimization algorithm like XMin6 [8], the main computational overhead comes from the for-loop where the (n − 1)-input variable function is ...
Many-Body effects in Semiconductor Nanostructures Stockholm University Licentiat Thesis
Many-Body effects in Semiconductor Nanostructures Stockholm University Licentiat Thesis

... These atom-like properties give the QD:s the name artificial atoms, and many theoretical methods used in atomic physics are also very suitable when handling them. But semiconductors can also be used to form more exotic potential structures, displaying qualities usually not found in nature. An exampl ...
Assessing the Nonequilibrium Thermodynamics in a
Assessing the Nonequilibrium Thermodynamics in a

An Introduction to Quantum Spin Systems Notes for MA5020 (John
An Introduction to Quantum Spin Systems Notes for MA5020 (John

arXiv:quant-ph/0510223v4 1 Jun 2007 Foundations Of Quantum
arXiv:quant-ph/0510223v4 1 Jun 2007 Foundations Of Quantum

K a - IDEALS @ Illinois
K a - IDEALS @ Illinois

`To Be, To Be, What Does it Mean to Be?` : On Quantum
`To Be, To Be, What Does it Mean to Be?` : On Quantum

Introduction to Black Hole Thermodynamics
Introduction to Black Hole Thermodynamics

Curriculum Vitae Irinel Chiorescu
Curriculum Vitae Irinel Chiorescu

The principle of a finite density of information
The principle of a finite density of information

... Moreover, yet another problem arises in the quantum setting, mamely that of ‘correlations’; which in this context are also referred to as ‘entanglement’. To understand what entanglement is, one must apply the superposition principle again, but this time to pairs of systems. For instance if systems A ...
Propensities in Quantum Mechanics - Philsci
Propensities in Quantum Mechanics - Philsci

Path Integrals in Quantum Mechanics Dennis V. Perepelitsa
Path Integrals in Quantum Mechanics Dennis V. Perepelitsa

Quantum Computational Renormalization in the - IAP TU
Quantum Computational Renormalization in the - IAP TU

On the Study of Quantum Properties of Space-Time with
On the Study of Quantum Properties of Space-Time with

... Abstract The expectation that it should not be possible to gain experimental insight on the structure of space-time at Planckian distance scales has been recently challenged by several studies which have shown that there are a few classes of experiments with sensitivity suÆcient for setting signi c ...
Quantum and classical statistics of the electromagnetic zero
Quantum and classical statistics of the electromagnetic zero

Last section - end of Lecture 4
Last section - end of Lecture 4

... In this final segment, I would like to describe some aspects of the gravitational effective field theory which need to be developed more fully in the future. We have seen how to quantize the theory and make quantum field theoretic predictions within general relativity. The most straightforward ampli ...
Geometry of the Set of Mixed Quantum States: An Apophatic Approach
Geometry of the Set of Mixed Quantum States: An Apophatic Approach

Evade the Heisenberg Uncertainty Principle
Evade the Heisenberg Uncertainty Principle

... electromagnetic radiation in all directions, which is detected and reconstructed as 3-D images or 2-D slice images. In a fundamental science laboratory, quantum state tomography is the process of completely characterizing the quantum state of an object as it is emitted by its source, before a possib ...
Solving Critical Section problem in Distributed system by Entangled Quantum bits
Solving Critical Section problem in Distributed system by Entangled Quantum bits

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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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