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

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.