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Fast Geometric Transformations on Quantum Images

Phuc Q. Le, Abdullah M. Iliyasu, Fangyan Dong

Published Jan 1, 2010• 107 citations
OpenAlex

Abstract

Circuits to achieve geometric trans- formations including two-point swapping, flip, co- ordinate swapping, orthogonal rotations and their variants on N -sized quantum images are proposed based on the basic quantum gates; NOT, CNOT and Toffoli gates. The complexity of the circuits is O(log 2 N) for two-point swapping and O(logN) for flip, co-ordinate swapping and orthogonal rotations. The results indicate that local operations like two- point swapping are slower than global operations like flip, co-ordinate swapping, and orthogonal rotations in quantum image processing. This is in contrast to performing such operations in classical image process- ing where the local operations are faster. All the proposed transformations are confirmed by simula- tion on a classical computer. With their low com- plexity, these geometric transformations can be used as the major components to build circuits for other applications on quantum images.

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Circuits to achieve geometric trans- formations including two-point swapping, flip, co- ordinate swapping, orthogonal rotations and their variants on N -sized quantum images are proposed based on the basic quantum gates; NOT, CNOT and Toffoli gates. The complexity of the circuits is O(log 2 N) for two-point swapping and O(logN) for flip, co-ordinate swapping and orthogonal rotations. The results indicate that local operations like two- point swapping are slower than global operations like flip, co-ordinate swapping, and orthogonal rotations in quantum image processing. This is in contrast to performing such operations in classical image process- ing where the local operations are faster. All the proposed transformations are confirmed by simula- tion on a classical computer. With their low com- plexity, these geometric transformations can be used as the major components to build circuits for other applications on quantum images.
The negative effects caused by geometric distortion can be removed to the maximum extent possible through appropriate geometric transformations, allowing us to focus on the image content itself in subsequent processing and recognition. Therefore, geometric transformations are often used as a pre-processing step for other image processing applications. In this paper, quantum algorithms are designed to implement geometric transformations, including two-point swapping, circular translation, flipping transformations and right-angle rotation, across a quantum image representation QIRHSI (Quantum Image Representation based on HSI color space) which is built on HSI (Hue-Saturation-Intensity) color space. The above geometric transformations are realized by quantum circuits composed of elementary quantum gates. By analyzing the complexity of the fundamental quantum gates needed for the above geometric transformations, it is found that the global transformations (circular translation, flip transformation and right-angle rotation) are lower than the local transformation (two-point swapping). The proposed geometric transforms are used to facilitate the applications of quantum images with low complexity and high efficiency.
Quantum circuits to realize color operations of channel of interest, channel swapping, and alpha blending on images are proposed using five kinds of quantum gates, i.e., NOT, CNOT, Toffoli, Rotation, and Controlled Rotation gates. Complexities of the proposed circuits are for an N-sized image, whereas the color information must be transformed pixel by pixel in the case of operators on classical computers. Simulations on the proposed three quantum color operations using three human facial and one Japanese style house images demonstrate that at most 9, 3, and 5 basic quantum gates are requested, that shows the feasibility of quantum circuits. Based on proposed three operations, all invertible classical color information transformation on imagesmay be designed and many applications can be realized on quantum computer, and the channel of interest based watermarking is being researched which the experiment results show that from the point of PSNR, our proposal is about 10 dB better than the chosen method of quantum image watermarking.
The article discusses the theory of quantum image processing and quantum image manipulation (fast geometric transformations, including the flip, orthogonal rotations and limited geometric transformations). In this work we have investigated the area of quantum image processing and have tested the FPQI model. Models (developed in the work and others) were investigated by explicitly modeling quantum behavior on a large quantum system. A basic scheme for encoding 2×2 image has been created to gain a clearer understanding of the mechanics of quantum image models.
To achieve a certain desired visual effect, the pixel positions of the input image are mapped to positions different from the original ones so as to change the display effect of the original image. This process is called the geometric transformation of the image. Geometric transformations mainly include geometric correction of the image and spatial transformations (scaling transformation, rotation, and affine transformation) [ 1 – 26 ] . This chapter proposes the geometric transformations of the QIRHSI quantum color image, including basic geometric transformations and scaling operations, and further studies the unitary operators of geometric transformations and the corresponding quantum circuits.
Quantum computing (Q-computing) is a revolutionary technology that applies quantum mechanics (QM) to implement calculations significantly quicker than traditional computers. Q-computing employs Q-bits (qubits) rather than classical bits, which can take on a superposition state, making it possible to represent both 0 and 1 at once. This distinctive property allows qubits to perform multiple calculations simultaneously, making Q computers exponentially more powerful as more qubits are added, compared to the linear scaling of classical computers. However, challenges such as qubit instability and error rates remain significant obstacles to fully realizing this potential. A vital aspect of this technology is the development of Q arithmetic circuits, which act as the foundational blocks for efficiently executing Q algorithms. Q half-adder and full-adder circuits are integral to performing fundamental arithmetic operations within Q computers. In contrast to classical adders, Q adders rely on Q gates such as Controlled NOT (CNOT) and Toffoli gates. These gates exploit Q phenomena like entanglement and superposition, allowing for more efficient computations. The Q half-adder consists of two qubits and adds two single bits, producing a carry and sum output. It utilizes the CNOT gate to compute the sum and the Toffoli gate (controlled-controlled-NOT [CCNOT]) to compute the carry. The Q full-adder builds on this, adding three qubits to produce a sum and a carry output, further increasing computational capacity. Through the entanglement of qubits, these circuits allow for more parallelism and speed compared to classical adders. Q carry-look-ahead adders (QCLAs) and Q ripple-carry adders (QRCAs) are used to perform more complex arithmetic operations based on these half-adder and full-adder structures. Q arithmetic circuits have broad applications, boosting secure key generation in cryptography, speeding up matrix operations for faster model training in machine learning, and enabling more precise, efficient image transformations in Q image processing, benefiting fields like medical imaging and computer vision.