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Recent advances in Quantum approach to Image processing

Quantum computer are becoming a reality with a race between IBM and Google trying to be the first to reach the so-called “Quantum Supremacy”. With this new soon available hardware, numerous quantum applications are appearing like quantum telecommunication,  and quantum computing is blooming. We have NV spin qubits which acts as quantum RAM, quantum microprocessor and quantum channels for communication, all what is needed to realize quantum information processing.

Quantum Information Processing focuses on information processing and computing based on quantum mechanics. While current digital computers encode data in binary digits (bits), quantum computers aren't limited to two states. They encode information as quantum bits, or qubits, which can exist in superposition. Qubits can be implemented with atoms, ions, photons or electrons and suitable control devices that work together to act as computer memory and a processor. Because a quantum computer can contain these multiple states simultaneously, they provide an inherent parallelism. This will enable them to solve certain problems much faster than any classical computer using the best currently known algorithms, like integer factorization or the simulation of quantum many-body systems. (Source: PicoQuant, Germany)

Quantum Information Processing is opening an entirely new domain of applications like faster digital image processing.  In fact, processing of digital images is very demanding in terms of 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.

Research team lead by Xi-Wei Yao and his team in China are working on Quantum Image Processing, an emerging sub-discipline that is focused on extending conventional image processing tasks and operations to the quantum computing framework [1]. They have shown the viability of a framework for quantum image processing. By encoding and processing the image information in quantum-mechanical systems, they demonstrate the efficiency of a pure quantum encoding of the image information: the pixel values are encoded in the probability amplitudes and the pixel positions in the computational basis states.

Comparison of image processing by classical and quantum computers. F and G are the input and output images, respectively. On the classical computer, an M × L image can be represented as a matrix and encoded with at least 2n bits. The classical image transformation is conducted by matrix computation. In contrast, the same image can be represented as a quantum state and encoded in n qubits. The quantum image transformation is performed by unitary evolution under a suitable Hamiltonian.

Their quantum image representation reduces the required number of qubits compared to existing implementations, and they presented an interesting image processing algorithms that provide exponential speed-up over their classical counterparts.

Another team from China presented an overview of the advances made in quantum image processing (QIP) [2]. In particular, they focused on recent progress on QIP-based security technologies including quantum watermarking, quantum image encryption, and quantum image steganography. The objectives of the discussions presented at the end of this study are twofold. First, targeting researchers already in the area, a few of the open questions. The second objective of the discussion is about some considerations that should guide upcoming researchers.

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