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How Quantum Theory Helps Us Explain
How Quantum Theory Helps Us Explain

... representative cases in which quantum theory has helped us explain otherwise puzzling phenomena. Apart from their diversity, three aspects of these cases are worth emphasizing. First, each case itself splits up into many sub-cases, corresponding to the variety of features we can use quantum theory t ...
Quantitative Determination of Singlet Oxygen Generated by Excited
Quantitative Determination of Singlet Oxygen Generated by Excited

Introduction to Quantum Computing (2010) (e-book)
Introduction to Quantum Computing (2010) (e-book)

... Information is something that can be encoded in the state of a physical system, and a computation is a task that can be performed with a physically realizable device. Therefore, since the physical world is fundamentally quantum mechanical, the foundations of information theory and computer science s ...
Introduction - The MIT Press
Introduction - The MIT Press

Two Qubits for CG Jung`s Theory of Personality
Two Qubits for CG Jung`s Theory of Personality

... [rational opposites], Sensation/iNtuition [irrational opposites]. Jung takes this twodimensional representation in order to demonstrate the dependencies between the psychological functions. For example, Thinking and Feeling are opposites and conflict with each other (assuming one fixed attitude). Ho ...
A proof that measured data and equations of quantum mechanics
A proof that measured data and equations of quantum mechanics

Quantum Proofs for Classical Theorems
Quantum Proofs for Classical Theorems

... Of course one can argue that applications of the probabilistic method are all just counting arguments disguised in the language of probability, and hence probabilistic arguments are not essential to the proof. In a narrow sense this is indeed correct. However, viewing things probabilistically gives ...
Quantum Computation by Adiabatic Evolution Edward Farhi, Jeffrey Goldstone Sam Gutmann
Quantum Computation by Adiabatic Evolution Edward Farhi, Jeffrey Goldstone Sam Gutmann

... In Section 2 we present the building blocks of our algorithm in detail. This includes some discussion of the adiabatic theorem and level crossings. In Section 3 we illustrate the method on a small example that has three clauses, each acting on 2 bits. Each 2-bit clause has more than one satisfying a ...
Machine Learning - Department of Computer Science
Machine Learning - Department of Computer Science

Machine Learning
Machine Learning

... Machine learning counts with many successful applications and is becoming increasingly important in science and industry. ...
Program and Booklet - Fakultät für Mathematik
Program and Booklet - Fakultät für Mathematik

polarized quantum states
polarized quantum states

Finite machines, mental procedures, and modern
Finite machines, mental procedures, and modern

Narrowband polarization-entangled photon pairs distributed over a
Narrowband polarization-entangled photon pairs distributed over a

Infinite-randomness quantum critical points induced by dissipation
Infinite-randomness quantum critical points induced by dissipation

... single cluster depends exponentially on the damping constant 共and thus on the cluster size兲, ⑀ = ␥⌳e−␲␥, as derived in Eq. 共8兲. We will come back to this point in Sec. V where we discuss the case of non-Ohmic dissipation. Although the prefactor in Eq. 共22兲 is larger than 1, this does not mean that t ...
Quantum Stabilizer Codes Embedding Qubits Into Qudits
Quantum Stabilizer Codes Embedding Qubits Into Qudits

... combat quantum errors to avoid their detrimental effect on quantum information processes. To this end, the method of error correcting codes has been borrowed from classical information theory (for a comprehensive introduction to the quantum theory of error correcting codes we refer to [2]). The unde ...
- Philsci
- Philsci

... in the dendritic spines that communicate with gap junctions. De Zeeuw et al. (1995) clearly show that DLBs are found tens of micrometers away from the actual gap junction couplings, and that there is only correlation between the existence of gap junctions and existence of DLBs in sense that DLBs m ...
INTRODUCTION TO NOISE AND DENSITY MATRICES. Slides in PPT.
INTRODUCTION TO NOISE AND DENSITY MATRICES. Slides in PPT.

... Imagine that a quantum system is in the state  j with Probability of outcome k being in state j probability pj . We do a measurement described by projectors Pk . ...
Introduction to Quantum Information
Introduction to Quantum Information

3 Ion Trap Implementations
3 Ion Trap Implementations

Quantum Lambda Calculus - Department of Mathematics and
Quantum Lambda Calculus - Department of Mathematics and

... must be accessed via their interface. In other words, a program may interact with a function-as-data by applying it to an argument, but not, for instance, by examining its code. We give some examples illustrating how some common phenomena in quantum computation can be interpreted in terms of higher- ...
Presentation
Presentation

Introduction to loop quantum gravity
Introduction to loop quantum gravity

... How does one think in loop quantum gravity? Loop quantum gravity is based on the assumption that quantum mechanics and general relativity are correct. It assumes background independence and does not attempt to unify forces, only to quantise gravity. Spacetime is assumed to be four-dimensional and no ...
Philosophy of Science, 69 (September 2002) pp
Philosophy of Science, 69 (September 2002) pp

... Fuchs and Peres (2000)). Their escape typically involves arguing that assuming the existence of hidden variables presupposes a form of realism or `objectivity' inappropriate in quantum theory. On the other hand, other physicists have recently taken to calculating the average number of classical bits ...
Opening up three quantum boxes causes classically undetectable
Opening up three quantum boxes causes classically undetectable

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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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