24,719 research outputs found
Quantum Image Processing and Its Application to Edge Detection: Theory and Experiment
Processing of digital images is continuously gaining in volume and relevance,
with concomitant demands on data storage, transmission and processing power.
Encoding the image information in quantum-mechanical systems instead of
classical ones and replacing classical with quantum information processing may
alleviate some of these challenges. By encoding and processing the image
information in quantum-mechanical systems, we here demonstrate the framework of
quantum image processing, where a pure quantum state encodes the image
information: we encode the pixel values in the probability amplitudes and the
pixel positions in the computational basis states. Our quantum image
representation reduces the required number of qubits compared to existing
implementations, and we present image processing algorithms that provide
exponential speed-up over their classical counterparts. For the commonly used
task of detecting the edge of an image, we propose and implement a quantum
algorithm that completes the task with only one single-qubit operation,
independent of the size of the image. This demonstrates the potential of
quantum image processing for highly efficient image and video processing in the
big data era.Comment: 13 pages, including 9 figures and 5 appendixe
Quantum computation and analysis of Wigner and Husimi functions: toward a quantum image treatment
We study the efficiency of quantum algorithms which aim at obtaining phase
space distribution functions of quantum systems. Wigner and Husimi functions
are considered. Different quantum algorithms are envisioned to build these
functions, and compared with the classical computation. Different procedures to
extract more efficiently information from the final wave function of these
algorithms are studied, including coarse-grained measurements, amplitude
amplification and measure of wavelet-transformed wave function. The algorithms
are analyzed and numerically tested on a complex quantum system showing
different behavior depending on parameters, namely the kicked rotator. The
results for the Wigner function show in particular that the use of the quantum
wavelet transform gives a polynomial gain over classical computation. For the
Husimi distribution, the gain is much larger than for the Wigner function, and
is bigger with the help of amplitude amplification and wavelet transforms. We
also apply the same set of techniques to the analysis of real images. The
results show that the use of the quantum wavelet transform allows to lower
dramatically the number of measurements needed, but at the cost of a large loss
of information.Comment: Revtex, 13 pages, 16 figure
Tensor Numerical Methods in Quantum Chemistry: from Hartree-Fock Energy to Excited States
We resume the recent successes of the grid-based tensor numerical methods and
discuss their prospects in real-space electronic structure calculations. These
methods, based on the low-rank representation of the multidimensional functions
and integral operators, led to entirely grid-based tensor-structured 3D
Hartree-Fock eigenvalue solver. It benefits from tensor calculation of the core
Hamiltonian and two-electron integrals (TEI) in complexity using
the rank-structured approximation of basis functions, electron densities and
convolution integral operators all represented on 3D
Cartesian grids. The algorithm for calculating TEI tensor in a form of the
Cholesky decomposition is based on multiple factorizations using algebraic 1D
``density fitting`` scheme. The basis functions are not restricted to separable
Gaussians, since the analytical integration is substituted by high-precision
tensor-structured numerical quadratures. The tensor approaches to
post-Hartree-Fock calculations for the MP2 energy correction and for the
Bethe-Salpeter excited states, based on using low-rank factorizations and the
reduced basis method, were recently introduced. Another direction is related to
the recent attempts to develop a tensor-based Hartree-Fock numerical scheme for
finite lattice-structured systems, where one of the numerical challenges is the
summation of electrostatic potentials of a large number of nuclei. The 3D
grid-based tensor method for calculation of a potential sum on a lattice manifests the linear in computational work, ,
instead of the usual scaling by the Ewald-type approaches
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