1,033 research outputs found
It Takes (Only) Two: Adversarial Generator-Encoder Networks
We present a new autoencoder-type architecture that is trainable in an
unsupervised mode, sustains both generation and inference, and has the quality
of conditional and unconditional samples boosted by adversarial learning.
Unlike previous hybrids of autoencoders and adversarial networks, the
adversarial game in our approach is set up directly between the encoder and the
generator, and no external mappings are trained in the process of learning. The
game objective compares the divergences of each of the real and the generated
data distributions with the prior distribution in the latent space. We show
that direct generator-vs-encoder game leads to a tight coupling of the two
components, resulting in samples and reconstructions of a comparable quality to
some recently-proposed more complex architectures
Quantum Algorithmic Gate-Based Computing: Grover Quantum Search Algorithm Design in Quantum Software Engineering
The difference between classical and quantum algorithms (QA) is following:
problem solved by QA is coded in the structure of the quantum operators. Input
to QA in this case is always the same. Output of QA says which problem coded.
In some sense, give a function to QA to analyze and QA returns its property as
an answer without quantitative computing. QA studies qualitative properties of
the functions. The core of any QA is a set of unitary quantum operators or
quantum gates. In practical representation, quantum gate is a unitary matrix
with particular structure. The size of this matrix grows exponentially with an
increase in the number of inputs, which significantly limits the QA simulation
on a classical computer with von Neumann architecture. Quantum search algorithm
(QSA) - models apply for the solution of computer science problems as searching
in unstructured data base, quantum cryptography, engineering tasks, control
system design, robotics, smart controllers, etc. Grovers algorithm is explained
in details along with implementations on a local computer simulator. The
presented article describes a practical approach to modeling one of the most
famous QA on classical computers, the Grover algorithm.Comment: arXiv admin note: text overlap with arXiv:quant-ph/0112105 by other
author
Fast quantum search algorithm modelling on conventional computers: Information analysis of termination problem
The simplest technique for simulating a quantum algorithm - QA described
based on the direct matrix representation of the quantum operators. Using this
approach, it is relatively simple to simulate the operation of a QA and to
perform fidelity analysis. A more efficient fast QA simulation technique is
based on computing all or part of the operator matrices on an as needed current
computational basis. Using this technique, it is possible to avoid storing all
or part of the operator matrices. The compute on demand approach benefits from
a study of the quantum operators, and their structure so that the matrix
elements can be computed more efficiently. Effective simulation of Grover
quantum search algorithm as example on computer with classical architecture is
considered
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