
Quantum Image Preparation Based on Exclusive SumofProduct Minimization and Ternary Trees
Quantum image processing is one of the promising fields of quantum infor...
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Quantum pixel representations and compression for Ndimensional images
We introduce a novel and uniform framework for quantum pixel representat...
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Tcount Optimized Quantum Circuits for Bilinear Interpolation
Quantum circuits for basic image processing functions such as bilinear i...
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Quantum advantage in learning from experiments
Quantum technology has the potential to revolutionize how we acquire and...
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A polynomial size model with implicit SWAP gate counting for exact qubit reordering
Due to the physics behind quantum computing, quantum circuit designers m...
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QRMW: Quantum representation of multi wavelength images
In this study, we propose quantum representation of multi wavelength ima...
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Quantum Psychothotonix
IMAGES are the building blocks of reality. External images are made up o...
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Improved FRQI on superconducting processors and its restrictions in the NISQ era
In image processing, the amount of data to be processed grows rapidly, in particular when imaging methods yield images of more than two dimensions or time series of images. Thus, efficient processing is a challenge, as data sizes may push even supercomputers to their limits. Quantum image processing promises to encode images with logarithmically less qubits than classical pixels in the image. In theory, this is a huge progress, but so far not many experiments have been conducted in practice, in particular on real backends. Often, the precise conversion of classical data to quantum states, the exact implementation, and the interpretation of the measurements in the classical context are challenging. We investigate these practical questions in this paper. In particular, we study the feasibility of the Flexible Representation of Quantum Images (FRQI). Furthermore, we check experimentally what is the limit in the current noisy intermediatescale quantum era, i.e. up to which image size an image can be encoded, both on simulators and on real backends. Finally, we propose a method for simplifying the circuits needed for the FRQI. With our alteration, the number of gates needed, especially of the errorprone controlledNOT gates, can be reduced. As a consequence, the size of manageable images increases.
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