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

Sanjit Krishna G M

Published Jul 15, 2025• 0 citations
Publisher

Abstract

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.

DOI
10.21203/rs.3.rs-7098110/v1

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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.
I propose a novel framework for quantum image storage using continuous-variable (CV) photonic systems. Unlike traditional qubit-based approaches, this model encodes grayscale image intensities into qumodes via coherent-state displacement operators. A delta evolution mechanism enables memory efficient storage by recording only intensity shifts between frames. To support scalable retrieval, I introduce entropy based frame indexing using von Neumann entropy. The proposed system is simulated using Strawberry Fields, demonstrating partial fidelity preservation and coherent phase-space behavior via Wigner function visualization. This approach offers a promising pathway toward scalable, photonic-compatible quantum memory models for quantum vision and imaging applications.
Modern information systems increasingly require memory architectures capable of storing not only static data but also the evolution of information over time. While continuous-variable photonic platforms have been extensively studied for quantum communication and computation, their potential as dynamically evolving memory systems remains largely unexplored. In this work, we introduce a metadata-assisted continuous-variable photonic memory architecture in which information is represented by latent vectors encoded across multimode coherent states and updated through reversible displacement operations. The framework combines multimode storage with metadata-based indexing, enabling navigation and reconstruction of historical memory states through rollback retrieval. Unlike conventional approaches that focus on preserving a single quantum state, the proposed framework treats memory as a continuously evolving trajectory in continuous-variable phase space. This perspective naturally extends beyond image storage to temporal data streams, machine-learning latent representations, scientific simulations, and other forms of dynamically changing information. Although presented as a theoretical proof-of-concept, the results suggest that continuous-variable photonic systems may provide a promising foundation for future metadata-aware memory architectures capable of storing, tracking, and reconstructing the history of evolving information.
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.
We investigate the storage and retrieval of an image in a multi-particle quantum mechanical system. Several models are studied and compared with corresponding classical digital methods. We consider a situation in which qubits replace classical bits in an array of pixels and show several advantages. For example, we consider the situation in which 4 different values are randomly stored in a single qubit and show that quantum mechanical properties allow better reproduction of original stored values compared with classical (even stochastic) methods. The retrieval process is uniquely quantum (involves measurement in more than one bases). The independence and the finiteness of the stored copies of the image play an important role in the quantum protocol being better than the classical one. Other advantages of quantum storage of an image are found in its security.
Quantum memory is one of the key components constituting a quantum network. An important step towards the successful development of such a network is the storage of single photons. Encoding photons in high-dimensional photonic states can significantly increase the network's information-carrying capacity. Furthermore, quantum memories that are able to store multiple optical modes offer advantages over single-mode memories, in terms of both speed and capacity. However, a practical implementation of such a scheme for storing and retrieving multiple images at the single-photon level has not yet been achieved. Here, we provide experimental evidence that two spatial probe fields imprinted with a real image each can be stored and retrieved with good visibility and similarity at the single-photon level in a 85{}^{85}85Rb cold atomic ensemble, making use of electromagnetically induced transparency. Our results are very promising for the realization of a high-dimensional quantum network in the future.
Forecasting complex processes requires efficient learning from temporal data. Reservoir computing platforms enable such learning with minimal training cost. Quantum reservoir computing (QRC) extends this framework into the quantum domain, offering promising capabilities for online, quantum-enhanced machine learning tailored to temporal tasks. As in the classical case, photonics provides a natural platform for QRC. However, implementing native memory capabilities in practical photonic quantum systems remains a major challenge. Here we demonstrate a photonic QRC platform based on deterministically generated multimode squeezed states, exploiting spectral and temporal multiplexing in a continuous-variable setting with controllable fading memory. Data is encoded via programmable pump phase shaping in an optical parametric process and retrieved through mode-selective homodyne detection. Real-time memory is implemented through feedback via electro-optic modulation, and expressivity is boosted via spatial multiplexing. This architecture enables nonlinear temporal tasks, including parity check at different delays and chaotic signal forecasting. All results are supported by a high-fidelity Digital Twin. Leveraging the entangled multimode structure enhances expressivity and memory capacity, establishing a scalable continuous-variable photonic platform for quantum-enhanced information processing.