2,253 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
Hierarchical quantum classifiers
Quantum circuits with hierarchical structure have been used to perform binary
classification of classical data encoded in a quantum state. We demonstrate
that more expressive circuits in the same family achieve better accuracy and
can be used to classify highly entangled quantum states, for which there is no
known efficient classical method. We compare performance for several different
parameterizations on two classical machine learning datasets, Iris and MNIST,
and on a synthetic dataset of quantum states. Finally, we demonstrate that
performance is robust to noise and deploy an Iris dataset classifier on the
ibmqx4 quantum computer
Synthesis of Quantum Logic Circuits
We discuss efficient quantum logic circuits which perform two tasks: (i)
implementing generic quantum computations and (ii) initializing quantum
registers. In contrast to conventional computing, the latter task is nontrivial
because the state-space of an n-qubit register is not finite and contains
exponential superpositions of classical bit strings. Our proposed circuits are
asymptotically optimal for respective tasks and improve published results by at
least a factor of two.
The circuits for generic quantum computation constructed by our algorithms
are the most efficient known today in terms of the number of expensive gates
(quantum controlled-NOTs). They are based on an analogue of the Shannon
decomposition of Boolean functions and a new circuit block, quantum
multiplexor, that generalizes several known constructions. A theoretical lower
bound implies that our circuits cannot be improved by more than a factor of
two. We additionally show how to accommodate the severe architectural
limitation of using only nearest-neighbor gates that is representative of
current implementation technologies. This increases the number of gates by
almost an order of magnitude, but preserves the asymptotic optimality of gate
counts.Comment: 18 pages; v5 fixes minor bugs; v4 is a complete rewrite of v3, with
6x more content, a theory of quantum multiplexors and Quantum Shannon
Decomposition. A key result on generic circuit synthesis has been improved to
~23/48*4^n CNOTs for n qubit
Quantum image classification using principal component analysis
We present a novel quantum algorithm for classification of images. The
algorithm is constructed using principal component analysis and von Neuman
quantum measurements. In order to apply the algorithm we present a new quantum
representation of grayscale images.Comment: 9 page
Nonnegative/binary matrix factorization with a D-Wave quantum annealer
D-Wave quantum annealers represent a novel computational architecture and
have attracted significant interest, but have been used for few real-world
computations. Machine learning has been identified as an area where quantum
annealing may be useful. Here, we show that the D-Wave 2X can be effectively
used as part of an unsupervised machine learning method. This method can be
used to analyze large datasets. The D-Wave only limits the number of features
that can be extracted from the dataset. We apply this method to learn the
features from a set of facial images
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
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