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Quantum DCT Amplitude Encoding for RGB Image Reconstruction: Shot-Budget Characterisation and a Unified Multi-Channel Formulation

Vamman Malik

Published May 30, 2026• 0 citations
Publisher

Abstract

We present a systematic empirical study of quantum amplitude encoding applied to the reconstruction of a full-colour photographic image via Discrete Cosine Transform (DCT) coefficients. A 256x256 RGB photograph is compressed by retaining the K lowest-frequency DCT coefficients per channel in zigzag scan order, whose magnitudes are loaded into an n-qubit quantum state using Qiskit's StatePreparation gate. We conduct a comprehensive sweep of n from 4 to 16 qubits at shot counts from 200,000 to 16,000,000, recording PSNR and SSIM at every combination. A principal finding is the PSNR dome: image quality peaks at intermediate n and falls as n increases further. We derive an optimality condition n* approximately log2(S/50). A unified RGB formulation encoding all three colour channels as a single 18-qubit amplitude state (K=262,144) is introduced, achieving PSNR=29.07 dB at 8M shots. All experiments run on the Qiskit Aer sampling simulator.

DOI
10.5281/zenodo.20459037

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We present a systematic empirical study of quantum amplitude encoding applied to the reconstruction of a full-colour photographic image via Discrete Cosine Transform (DCT) coefficients. A 256x256 RGB photograph is compressed by retaining the K lowest-frequency DCT coefficients per channel in zigzag scan order, whose magnitudes are loaded into an n-qubit quantum state using Qiskit's StatePreparation gate. We conduct a comprehensive sweep of n from 4 to 16 qubits at shot counts from 200,000 to 16,000,000, recording PSNR and SSIM at every combination. A principal finding is the PSNR dome: image quality peaks at intermediate n and falls as n increases further. We derive an optimality condition n* approximately log2(S/50). A unified RGB formulation encoding all three colour channels as a single 18-qubit amplitude state (K=262,144) is introduced, achieving PSNR=29.07 dB at 8M shots. All experiments run on the Qiskit Aer sampling simulator.
The Discrete Cosine Transform (DCT) is an integral part of classical image compression, which becomes the basis of the JPEG standard. With advances in quantum computing, there is growing interest in exploring quantum analogues of classical algorithms for such transforms. This paper presents a simulation of the Quantum Discrete Cosine Transform (QDCT) using the Qiskit Python library, applied to standard grayscale images segmented into 8x8 and 4x4 blocks. The QDCT algorithm is formulated as a unitary operator and evaluated using state-vector simulation. Due to the current constraints of quantum hardware and challenges in circuit synthesis, this study focused on simulation results, as direct quantum circuit measurement leads to amplitude collapse and unusable output. The performance of QDCT is quantitative compared to classical DCT using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and compression ratio metrics. The result of this study reveals the feasibility and current limitations of QDCT-based image compression while providing a reproducible benchmark for future quantum image processing research. This study offers a realistic assessment of the potential and technical limitations of QDCT in the context of emerging quantum technologies.
Abstract Quantum image processing requires color representations that are compact in quantum registers and recoverable from finite-shot measurement statistics. Existing quantum image representations often emphasize storage efficiency, whereas the geometric organization of color information and its effect on reconstruction remain less explored. Here we propose a Bloch-sphere-based quantum color space that maps discrete RGB values to single-qubit state parameters (θ , φ). Colors are organized by maximum–minimum channel subgroups: each subgroup is assigned to a latitude band, and channel-permutation trajectories are encoded by the azimuthal angle. The construction also induces a canonical grayscale subspace. Finite-shot measurement counts are converted into angle estimates and decoded through a deterministic region-of-point/lookup-table (RoP/LUT) rule. In Qiskit Aer simulations, MNIST grayscale reconstruction achieves an average mean squared error (MSE) of 3.90 and peak signal-to-noise ratio (PSNR) of 42.44 dB. On CIFAR-10, the 64 3 RGB grid achieves the best reconstruction performance, with MSE 81.29, PSNR 29.84 dB, and structural similarity (SSIM) 0.9313. Shot-wise experiments show that pixel-wise validation benefits from increased measurement budgets, whereas position-register full-image encoding is limited by sparse per-position measurement counts.
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.
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..
Multi-qubit encoding is a quantum encoding technique that uses multiple qubits to represent information, enabling higher data throughput. This study explores its application in compressed image transmission, comparing its performance to single-qubit encoding in both uncoded and channel-coded configurations. For image compression, JPEG and HEIF formats are used, with source coding rates adjusted to ensure similar bandwidth utilization. The compressed bitstream is optionally protected by channel coding before undergoing quantum encoding using qubit sizes from 1 to 8 for quantum channel transmission. At the receiver, quantum decoding reconstructs classical bits, followed by optional channel decoding, and finally, source decoding restores the image. Image quality is assessed using peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM) and universal quality index (UQI). Results show that increasing the qubit encoding size improves noise resilience, with eight-qubit encoding achieving a maximum channel SNR gain of 15 dB for JPEG and 17 dB for HEIF under channel coding, demonstrating greater robustness compared to single-qubit encoding but with higher computational complexity. To address this, an adaptive multi-qubit encoding process is proposed, dynamically adjusting qubit usage by utilizing fewer qubits for lower noise levels and higher qubits for higher noise conditions while balancing noise resilience and computational complexity under the assumption of perfect channel estimation.
Discrete Cosine Transform (DCT) is very important in image compression. Classical 1-D DCT and 2-D DCT has time complexity O(NlogN) and O(N²logN) respectively. This paper presents a quantum DCT iteration, and constructs a quantum 1-D and 2-D DCT algorithm for image compression by using the iteration. The presented 1-D and 2-D DCT has time complexity O(sqrt(N)) and O(N) respectively. In addition, the method presented in this paper generalizes the famous Grover's algorithm to solve complex unstructured search problem.