Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

arXiv:2502.17815

Novel quantum circuit for image compression utilizing modified Toffoli gate and quantized transformed coefficient alongside a novel reset gate

Md Ershadul Haque, Manoranjan Paul

Published Feb 25, 2025• 0 citations
arXivHTMLPDF

Abstract

Quantum image computing has emerged as a groundbreaking field, revolutionizing how we store and process data at speeds incomparable to classical methods. Nevertheless, as image sizes expand, so does the complexity of qubit connections, posing significant challenges in the efficient representation and compression of quantum images. In response, we introduce a modified Toffoli gate state connection using a quantized transform coefficient preparation process. This innovative strategy streamlines circuit complexity by modifying state connection from the state connection information. In our operational control gates, only input 1 impacts the output, allowing us to modify the state connection and dramatically enhance the efficiency of the proposed circuit. As a result, the proposed approach significantly reduces the number of gates required for both image compression and representation. Our findings reveal that it requires an impressive 44.21 percent fewer gates than existing techniques, such as the Direct Cosine Transform Efficient Flexible Representation of Quantum Images (DCTEFRQI), all while maintaining a consistent peak signal-to-noise ratio (PSNR). For an image block size of 2^Sx2^Sy with q gray levels, the complexity of our approach can be succinctly expressed as, O[3q+log2Sx+log2Sy+2q(log2Sx+log2Sy)]. Here, Sx and Sy represent the X and Y positional control gates while q indicates the non-zero transform coefficients. Moreover, experimental evaluations strongly demonstrate that it excels in both compressing and representing quantum images compared to the DCTEFRQI approach, particularly excelling in the essential metrics of gate requirements and PSNR performance. Embrace the future of quantum imaging with our innovative solution, where efficiency meets excellence.

DOI
10.48550/arxiv.2502.17815

Related papers

Efficient image representation and compression for quantum computing by exploiting correlations within qubits and connections

Md Ershadul Haque

In recent years, quantum image computing has gained significant attention due to its ability to provide more data storage and process faster than classical computation. These are possible due to the unique properties of quantum mechanism of parallelism, superposition, and entanglement. Compressing and representing classical image data in the quantum domain, as well as state label preparation complexity, is still a challenging issue. The existing techniques typically directly connect the pixel values and their corresponding state label connection without exploring quantum bits i.e. qubits and connection pattern correlation. As a result, the required number of quantum resources, such as gates and qubits to implement quantum circuits, is quite high. This thesis proposes several novel approaches by exploiting correlations within qubits/connections in the state preparation within pixel domain and transform domain. The existing quantum methods need qubits, and quantum gates to represent both pixel intensity and their corresponding position i.e. state preparation information. To reduce the number of qubits and gates required for state preparation, transformation and quantization are applied to the image data. This is because the transformed coefficients are concentrated in a smaller region compared to the pixel domain, allowing the number of state-preparation qubits to be significantly lower. Theoretical analysis and experimental results show that it provides better compression and representation than existing approaches. To represent the quantized transformed coefficient using the existing quantum representing approach, two control gates are needed, one for opening and another for closing the connection to locate each quantized transformed state value. Due to the repetitive use of the same control gates to locate each quantized state connection, a significant number of quantum resources are still required. Furthermore, resetting the output effect of each quantized transformed coefficient state connection on the qubit introduces redundancy in control gate usage and increases circuit complexity. To address state complexity challenges of the quantized transform coefficient approach, a novel reset gate is introduced that can perform the same task for closing the connection of state position. It modifies the closing state connection that nullifies the previous quantized transformed coefficient state connection effect on the next one, eliminating the need for repetitive use of control gates. Although using the reset gate instead of control gate, the proposed approach improves the compression performance, still, this approach requires higher quantum resources to locate the state position of the quantized transformed coefficient alongside. To address this, a novel control gate state connection approach is proposed. It modifies the quantized transformed coefficient connections by discarding ‘0’ values in the state preparation stage, since only input ‘1’ influences the output. This approach significantly reduces the circuit complexity. The experimental results confirm its superior performance in quantum image compression and representation. The required number of qubits for representing large magnitude of coefficient is sometimes higher for high quality images. To address this, a non-zero state connection of pixel values circuit is proposed on top of the block truncation coding compression approach. This approach is easy to implement, cost-effective, and memory-efficient and requires fewer qubits compared to the transformed-based approach. However, this approach requires more connections to complete the whole image circuit. Recently, an autoencoder-based approach has been used to compress quantum images. The main limitation of the autoencoder approach is that it is only applicable to small-scale images with unclear state compression mechanisms. To overcome this limitation, a parameterized position-aware lossy quantum autoencoder is proposed by utilizing the least significant bit (LSB) as a control qubit for optimized state-label information management. We have used a block-based position-aware LSB control qubit approach to improve further compression efficiency. Comparative analysis shows that it outperforms the existing quantum autoencoder approach in terms of gate efficiency as well as image quality.

•Dec 10, 2025•0 citations

A Novel State Connection Strategy for Quantum Computing to Represent and Compress Digital Images

Md Ershadul Haque, Manoranjan Paul, Anwaar Ulhaq +1 more

Quantum image processing draws a lot of attention due to faster data computation and storage compared to classical data processing systems. Converting classical image data into the quantum domain and state label preparation complexity is still a challenging issue. The existing techniques normally connect the pixel values and the state position directly. Recently, the EFRQI (efficient flexible representation of the quantum image) approach uses an auxiliary qubit that connects the pixel-representing qubits to the state position qubits via Toffoli gates to reduce state connection. Due to the twice use of Toffoli gates for each pixel connection still it requires a significant number of bits to connect each pixel value. In this paper, we propose a new SCMFRQI (state connection modification FRQI) approach for further reducing the required bits by modifying the state connection using a reset gate rather than repeating the use of the same Toffoli gate connection as a reset gate. Moreover, unlike other existing methods, we compress images using block-level for further reduction of required qubits. The experimental results confirm that the proposed method outperforms the existing methods in terms of both image representation and compression points of view.

arXiv:2212.07079•IEEE International Conference on Acoustics Speech and Signal Processing•May 5, 2023•4 citations

Block-Wise Compression Of The Quantum Gray-Scale Image Using Lossy Preparation Approach

Md Ershadul Haque, Manoranjan Paul

Quantum computing draws huge attention due to the multidimensional information processing capability of the processor at the same time. The main idea of quantum imaging is to represent pixel intensities and their state using standard quantum circuits. For a medium or larger size image, the state label circuit takes more qubits as well as requires a huge connection leading to circuit complexity. In this work, a block-wise lossy SCMNEQR (state connection modification novel enhanced quantum representation) approach has been proposed to address more qubit connection issues. The average computation time decreased by 99.66% and 7.39% compared to the JPEG and DCT-EFRQI approaches respectively. The experimental results show that it outperforms the DCT-EFRQI (discrete cosine transform- efficient flexible representation of the quantum image) approach in terms of both representation and compression due to the use of block-wise division of transfer coefficient and state label alongside a novel reset gate.

IEEE International Conference on Multimedia and Expo (ICME)•Jul 15, 2024•2 citations

Advanced quantum image representation and compression using a DCTEFRQI approach

Md Ershadul Haque, Manoranjan Paul, Anwar Ulhoq +1 more

Abstract In recent years, quantum image processing got a lot of attention in the field of image processing due to the opportunity to place huge image data in quantum Hilbert space. Hilbert space or Euclidean space has infinite dimensions to locate and process the image data faster. Moreover, several types of research show that the computational time of the quantum process is faster than classical computers. Encoding and compressing the image in the quantum domain is still a challenging issue. From the literature survey, we have proposed a DCT-EFRQI (Direct Cosine Transform Efficient Flexible Representation of Quantum Image) algorithm to represent and compress gray image efficiently which save computational time and minimize the complexity of preparation. This work aims to represent and compress various gray image sizes in quantum computers using the DCT (Discrete Cosine Transform) and EFRQI (Efficient Flexible Representation of Quantum Image) approaches together. The Quirk simulation tool is used to design the corresponding quantum image circuit. Due to the limitation of qubits, a total of 16 numbers of qubits is used to represent the coefficient and its position of the grayscale image. Among those, 8 number of qubits are used to map the coefficient values and the rest are used to generate the corresponding coefficient XY-coordinate position. Theoretical analysis and experimental results show that the proposed DCT-EFRQI scheme provides better representation and compression compared to DCT-GQIR, DWT-GQIR, and DWT-EFRQI in terms of PSNR(Peak Signal to Noise Ratio), and bit rate.

•Oct 14, 2022•0 citations

A New Way to Represent an Image in the Quantum Circuit for Compression and Representation Perspective

Md Ershadul Haque, Faranak Tohidi, Anwaar Ulhaq

Quantum image processing has recently received much attention due to the opportunity to place image data in quantum mechanics systems. The literature survey has shown that the complexity of preparing image data raises the question of how and in what efficient way a medium- and high-size image is represented inside the quantum domain, which is still a big question. Quantum properties such as entanglement, superposition, and parallelism make the quantum system faster and more robust. Qubit is the central basic unit that maps the image data in the quantum processor, which propagates from left to right. The number of qubits and their connection determine the efficiency of the circuit architecture. This chapter aims to investigate the performance of the transformed coefficient-based quantum image representation and compression approaches in terms of the required number of gates and PSNR values. Moreover, the challenges and prospects of quantum image compression and processing will be discussed as a future research direction.

IGI Global eBooks•May 1, 2025•0 citations

Advance quantum image representation and compression using DCTEFRQI approach

Md Ershadul Haque, Manoranjon Paul, Anwaar Ulhaq +1 more

In recent year, quantum image processing got a lot of attention in the field of image processing due to opportunity to place huge image data in quantum Hilbert space. Hilbert space or Euclidean space has infinite dimension to locate and process the image data faster. Moreover, several researches show that, the computational time of quantum process is faster than classical computer. By encoding and compressing the image in quantum domain is still challenging issue. From literature survey, we have proposed a DCTEFRQI (Direct Cosine Transform Efficient Flexible Representation of Quantum Image) algorithm to represent and compress gray image efficiently which save computational time and minimize the complexity of preparation. The objective of this work is to represent and compress various gray image size in quantum computer using DCT(Discrete Cosine Transform) and EFRQI (Efficient Flexible Representation of Quantum Image) approach together. Quirk simulation tool is used to design corresponding quantum image circuit. Due to limitation of qubit, total 16 numbers of qubit are used to represent the gray scale image among those 8 are used to map the coefficient values and the rest 8 are used to generate the corresponding coefficient position. Theoretical analysis and experimental result show that, proposed DCTEFRQI scheme provides better representation and compression compare to DCT-GQIR, DWT-GQIR and DWT-EFRQI in terms of PSNR(Peak Signal to Noise Ratio) and bit rate..

arXiv:2208.14277•Aug 30, 2022•1 citation
Quantum image computing has emerged as a groundbreaking field, revolutionizing how we store and process data at speeds incomparable to classical methods. Nevertheless, as image sizes expand, so does the complexity of qubit connections, posing significant challenges in the efficient representation and compression of quantum images. In response, we introduce a modified Toffoli gate state connection using a quantized transform coefficient preparation process. This innovative strategy streamlines circuit complexity by modifying state connection from the state connection information. In our operational control gates, only input 1 impacts the output, allowing us to modify the state connection and dramatically enhance the efficiency of the proposed circuit. As a result, the proposed approach significantly reduces the number of gates required for both image compression and representation. Our findings reveal that it requires an impressive 44.21 percent fewer gates than existing techniques, such as the Direct Cosine Transform Efficient Flexible Representation of Quantum Images (DCTEFRQI), all while maintaining a consistent peak signal-to-noise ratio (PSNR). For an image block size of 2^Sx2^Sy with q gray levels, the complexity of our approach can be succinctly expressed as, O[3q+log2Sx+log2Sy+2q(log2Sx+log2Sy)]. Here, Sx and Sy represent the X and Y positional control gates while q indicates the non-zero transform coefficients. Moreover, experimental evaluations strongly demonstrate that it excels in both compressing and representing quantum images compared to the DCTEFRQI approach, particularly excelling in the essential metrics of gate requirements and PSNR performance. Embrace the future of quantum imaging with our innovative solution, where efficiency meets excellence.
In recent years, quantum image computing has gained significant attention due to its ability to provide more data storage and process faster than classical computation. These are possible due to the unique properties of quantum mechanism of parallelism, superposition, and entanglement. Compressing and representing classical image data in the quantum domain, as well as state label preparation complexity, is still a challenging issue. The existing techniques typically directly connect the pixel values and their corresponding state label connection without exploring quantum bits i.e. qubits and connection pattern correlation. As a result, the required number of quantum resources, such as gates and qubits to implement quantum circuits, is quite high. This thesis proposes several novel approaches by exploiting correlations within qubits/connections in the state preparation within pixel domain and transform domain. The existing quantum methods need qubits, and quantum gates to represent both pixel intensity and their corresponding position i.e. state preparation information. To reduce the number of qubits and gates required for state preparation, transformation and quantization are applied to the image data. This is because the transformed coefficients are concentrated in a smaller region compared to the pixel domain, allowing the number of state-preparation qubits to be significantly lower. Theoretical analysis and experimental results show that it provides better compression and representation than existing approaches. To represent the quantized transformed coefficient using the existing quantum representing approach, two control gates are needed, one for opening and another for closing the connection to locate each quantized transformed state value. Due to the repetitive use of the same control gates to locate each quantized state connection, a significant number of quantum resources are still required. Furthermore, resetting the output effect of each quantized transformed coefficient state connection on the qubit introduces redundancy in control gate usage and increases circuit complexity. To address state complexity challenges of the quantized transform coefficient approach, a novel reset gate is introduced that can perform the same task for closing the connection of state position. It modifies the closing state connection that nullifies the previous quantized transformed coefficient state connection effect on the next one, eliminating the need for repetitive use of control gates. Although using the reset gate instead of control gate, the proposed approach improves the compression performance, still, this approach requires higher quantum resources to locate the state position of the quantized transformed coefficient alongside. To address this, a novel control gate state connection approach is proposed. It modifies the quantized transformed coefficient connections by discarding ‘0’ values in the state preparation stage, since only input ‘1’ influences the output. This approach significantly reduces the circuit complexity. The experimental results confirm its superior performance in quantum image compression and representation. The required number of qubits for representing large magnitude of coefficient is sometimes higher for high quality images. To address this, a non-zero state connection of pixel values circuit is proposed on top of the block truncation coding compression approach. This approach is easy to implement, cost-effective, and memory-efficient and requires fewer qubits compared to the transformed-based approach. However, this approach requires more connections to complete the whole image circuit. Recently, an autoencoder-based approach has been used to compress quantum images. The main limitation of the autoencoder approach is that it is only applicable to small-scale images with unclear state compression mechanisms. To overcome this limitation, a parameterized position-aware lossy quantum autoencoder is proposed by utilizing the least significant bit (LSB) as a control qubit for optimized state-label information management. We have used a block-based position-aware LSB control qubit approach to improve further compression efficiency. Comparative analysis shows that it outperforms the existing quantum autoencoder approach in terms of gate efficiency as well as image quality.
Quantum image processing draws a lot of attention due to faster data computation and storage compared to classical data processing systems. Converting classical image data into the quantum domain and state label preparation complexity is still a challenging issue. The existing techniques normally connect the pixel values and the state position directly. Recently, the EFRQI (efficient flexible representation of the quantum image) approach uses an auxiliary qubit that connects the pixel-representing qubits to the state position qubits via Toffoli gates to reduce state connection. Due to the twice use of Toffoli gates for each pixel connection still it requires a significant number of bits to connect each pixel value. In this paper, we propose a new SCMFRQI (state connection modification FRQI) approach for further reducing the required bits by modifying the state connection using a reset gate rather than repeating the use of the same Toffoli gate connection as a reset gate. Moreover, unlike other existing methods, we compress images using block-level for further reduction of required qubits. The experimental results confirm that the proposed method outperforms the existing methods in terms of both image representation and compression points of view.
Quantum computing draws huge attention due to the multidimensional information processing capability of the processor at the same time. The main idea of quantum imaging is to represent pixel intensities and their state using standard quantum circuits. For a medium or larger size image, the state label circuit takes more qubits as well as requires a huge connection leading to circuit complexity. In this work, a block-wise lossy SCMNEQR (state connection modification novel enhanced quantum representation) approach has been proposed to address more qubit connection issues. The average computation time decreased by 99.66% and 7.39% compared to the JPEG and DCT-EFRQI approaches respectively. The experimental results show that it outperforms the DCT-EFRQI (discrete cosine transform- efficient flexible representation of the quantum image) approach in terms of both representation and compression due to the use of block-wise division of transfer coefficient and state label alongside a novel reset gate.
Abstract In recent years, quantum image processing got a lot of attention in the field of image processing due to the opportunity to place huge image data in quantum Hilbert space. Hilbert space or Euclidean space has infinite dimensions to locate and process the image data faster. Moreover, several types of research show that the computational time of the quantum process is faster than classical computers. Encoding and compressing the image in the quantum domain is still a challenging issue. From the literature survey, we have proposed a DCT-EFRQI (Direct Cosine Transform Efficient Flexible Representation of Quantum Image) algorithm to represent and compress gray image efficiently which save computational time and minimize the complexity of preparation. This work aims to represent and compress various gray image sizes in quantum computers using the DCT (Discrete Cosine Transform) and EFRQI (Efficient Flexible Representation of Quantum Image) approaches together. The Quirk simulation tool is used to design the corresponding quantum image circuit. Due to the limitation of qubits, a total of 16 numbers of qubits is used to represent the coefficient and its position of the grayscale image. Among those, 8 number of qubits are used to map the coefficient values and the rest are used to generate the corresponding coefficient XY-coordinate position. Theoretical analysis and experimental results show that the proposed DCT-EFRQI scheme provides better representation and compression compared to DCT-GQIR, DWT-GQIR, and DWT-EFRQI in terms of PSNR(Peak Signal to Noise Ratio), and bit rate.
Quantum image processing has recently received much attention due to the opportunity to place image data in quantum mechanics systems. The literature survey has shown that the complexity of preparing image data raises the question of how and in what efficient way a medium- and high-size image is represented inside the quantum domain, which is still a big question. Quantum properties such as entanglement, superposition, and parallelism make the quantum system faster and more robust. Qubit is the central basic unit that maps the image data in the quantum processor, which propagates from left to right. The number of qubits and their connection determine the efficiency of the circuit architecture. This chapter aims to investigate the performance of the transformed coefficient-based quantum image representation and compression approaches in terms of the required number of gates and PSNR values. Moreover, the challenges and prospects of quantum image compression and processing will be discussed as a future research direction.
In recent year, quantum image processing got a lot of attention in the field of image processing due to opportunity to place huge image data in quantum Hilbert space. Hilbert space or Euclidean space has infinite dimension to locate and process the image data faster. Moreover, several researches show that, the computational time of quantum process is faster than classical computer. By encoding and compressing the image in quantum domain is still challenging issue. From literature survey, we have proposed a DCTEFRQI (Direct Cosine Transform Efficient Flexible Representation of Quantum Image) algorithm to represent and compress gray image efficiently which save computational time and minimize the complexity of preparation. The objective of this work is to represent and compress various gray image size in quantum computer using DCT(Discrete Cosine Transform) and EFRQI (Efficient Flexible Representation of Quantum Image) approach together. Quirk simulation tool is used to design corresponding quantum image circuit. Due to limitation of qubit, total 16 numbers of qubit are used to represent the gray scale image among those 8 are used to map the coefficient values and the rest 8 are used to generate the corresponding coefficient position. Theoretical analysis and experimental result show that, proposed DCTEFRQI scheme provides better representation and compression compare to DCT-GQIR, DWT-GQIR and DWT-EFRQI in terms of PSNR(Peak Signal to Noise Ratio) and bit rate..