Deep learning as a tool for quantum error reduction in quantum image
  processing
Krzysztof Werner Kamil Wereszczyński Rafał Potempa Krzysztof Cyran
Despite the limited availability and quantum volume of quantum computers,
quantum image representation is a widely researched area. Currently developed
methods use quantum entanglement to encode information about pixel positions.
These methods range from using the angle parameter of the rotation gate (e.g.,
the Flexible Representation of Quantum Images, FRQI), sequences of qubits
(e.g., Novel Enhanced Quantum Representation, NEQR), or the angle parameter of
the phase shift gates (e.g., Local Phase Image Quantum Encoding, LPIQE) for
storing color information. All these methods are significantly affected by
decoherence and other forms of quantum noise, which is an inseparable part of
quantum computing in the noisy intermediate-scale quantum era. These phenomena
can highly influence the measurements and result in extracted images that are
visually dissimilar to the originals. Because this process is at its foundation
quantum, the computational reversal of this process is possible. There are many
methods for error correction, mitigation, and reduction, but all of them use
quantum computer time or additional qubits to achieve the desired result. We
report the successful use of a generative adversarial network trained for
image-to-image translation, in conjunction with Phase Distortion Unraveling
error reduction method, for reducing overall error in images encoded using
LPIQE.