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Quantum Information Processing

Image storage, retrieval, compression and segmentation in a quantum system

Hai-Sheng Li, Zhu Qingxin, Song Lan, Chenyi Shen, Ri‐Gui Zhou, Mo Jia

Published Jan 8, 2013• 158 citations
Publisher
DOI
10.1007/s11128-012-0521-5

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Qumode-Based Quantum Image Storage with Entropy-Guided Frame Indexing and Fidelity-Preserved Retrieval

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Abstract Through this paper I propose a novel quantum image storage framework that leverages qumodes (continuous-variable photonic modes) as a medium for encoding classical image data. Unlike traditional qubit-based quantum image models this method introduces a dynamic, intensity-evolving structure where pixel values are stored as displacement amplitudes in qumode states. The innovation lies in delta-evolved encoding scheme, where each image or frame builds upon the intensity of a base qumode using additive displacement operators, thus reducing the need for fresh physical qubits or qumodes for every image. To distinguish and retrieve stored images or video frames, I introduce quantum entropy-based indexing, utilizing von Neumann entropy as a unique, low-overhead identifier of the informational content embedded in each qumode state. This dual mechanism of delta encoding and entropy fingerprinting enables memory efficient, scalable, and temporally trackable storage of multiple classical images in photonic quantum systems. This proposed model is not only theoretically sound but also practical for near-term implementation on continuous-variable quantum platforms such as Strawberry Fields. Simulations demonstrate the feasibility of RGB encoding, entropy tracking and potential compression advantages.

•Jul 15, 2025•0 citations
A novel method is proposed in this paper to store and retrieve images in entangled quantum systems, i.e., we employ a 2-qubit (or 3-qubit) entangled quantum state to represent the colors of 3 pixels (or 5 pixels) of color or gray images, and design quantum circuits to store images in entangled quantum systems, in addition, illustrate how to retrieve images stored in entangled quantum systems and analyze that the number of measurement is needed at most to retrieve a correct entangled quantum state. Simulation results on the Lena image shows compression ratio is 5.7 when PSNR is 32.8DB by Fourier transform while compression ratio of 2-qubit entangled coding (i.e., 2-qubit entangled quantum states represent image colors) is 12 without losing any information of the image. Due to take advantage of the superposition and entanglement properties of quantum states, our method improves the ability of image compression.
Abstract Through this paper I propose a novel quantum image storage framework that leverages qumodes (continuous-variable photonic modes) as a medium for encoding classical image data. Unlike traditional qubit-based quantum image models this method introduces a dynamic, intensity-evolving structure where pixel values are stored as displacement amplitudes in qumode states. The innovation lies in delta-evolved encoding scheme, where each image or frame builds upon the intensity of a base qumode using additive displacement operators, thus reducing the need for fresh physical qubits or qumodes for every image. To distinguish and retrieve stored images or video frames, I introduce quantum entropy-based indexing, utilizing von Neumann entropy as a unique, low-overhead identifier of the informational content embedded in each qumode state. This dual mechanism of delta encoding and entropy fingerprinting enables memory efficient, scalable, and temporally trackable storage of multiple classical images in photonic quantum systems. This proposed model is not only theoretically sound but also practical for near-term implementation on continuous-variable quantum platforms such as Strawberry Fields. Simulations demonstrate the feasibility of RGB encoding, entropy tracking and potential compression advantages.