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Despite the limited availability and quantum volume of quantum computers,quantum image representation is a widely researched area. Currently developedmethods 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 ofthe phase shift gates (e.g., Local Phase Image Quantum Encoding, LPIQE) forstoring color information. All these methods are significantly affected bydecoherence and other forms of quantum noise, which is an inseparable part ofquantum computing in the noisy intermediate-scale quantum era. These phenomenacan highly influence the measurements and result in extracted images that arevisually dissimilar to the originals. Because this process is at its foundationquantum, the computational reversal of this process is possible. There are manymethods for error correction, mitigation, and reduction, but all of them usequantum computer time or additional qubits to achieve the desired result. Wereport the successful use of a generative adversarial network trained forimage-to-image translation, in conjunction with Phase Distortion Unravelingerror reduction method, for reducing overall error in images encoded usingLPIQE.

Deep learning as a tool for quantum error reduction in quantum image processing