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arXiv:2607.18702

A Reversible Continuous-Variable Photonic Memory Architecture Based on Displacement-Evolved Coherent-State Dynamics

Sanjit Krishna G M

quant-phSubmitted Jul 21, 2026• Announced Jul 22, 2026• 0 citations
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Abstract

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.

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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.
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.
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.
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.
ABSTRACT:This paper presents a complete theoretical framework for the long-duration macroscopic storage and high-fidelity retrieval of multi-mode optical image data within a bounded physical enclosure. By mitigating the fundamental constraints of entropic decay and classical wave scattering, we demonstrate that a combined architecture utilizing Electromagnetically Induced Transparency (EIT), a Periodic Dynamical Decoupling (PDD) pulse matrix, and a Quantum Phase Conjugate Matrix (QPCM) can actively preserve spatial coherence. The physical storage medium is modeled under realistic thermodynamic constraints using a vapor cell containing a high density of gaseous 87Rb atoms operating at an ambient enclosure temperature of T = 310.15 K (37°C) and an atomic number density of ρ ≈ 2.4 x 10^11 atoms/cm^3. We provide a complete mathematical formulation of the storage, noise cancellation, and wave-reversal mechanisms using multi-variable quantum mechanics and Bra-Ket state vector notation. The model is computationally validated by a custom numerical simulation script that tracks the phase decay dampening vectors across a 50-pulse iteration sequence. The simulation demonstrates that the residual phase noise dampens down directly to a near-zero limit of 0.0009 rad (converging perfectly to 0.00 rad), confirming absolute phase stabilization and information preservation without active feedback loops. Furthermore, we outline how this spatial coherence containment architecture scales to satisfy the operational limits of high-capacity Quantum Computational Memory networks and robust Deep Space Communication Buffers. CODE VERIFICATION NODE:The functional Python numerical simulation verification script and version control tracking history are publicly hosted and available for auditing at: https://github.com/viren-dabas/Quantum-Phased-Information-Recovery
Quantum reservoir computing (QRC) leverages the nonlinearity and correlations of quantum systems to enhance machine learning tasks. We explore continuous-variable (CV) photonic platforms for QRC using frequency- and pulse-multiplexed squeezed states generated via nonlinear waveguides, enabling scalable, room-temperature architectures without single-photon sources or detectors. We introduce a novel, measurement-based QRC protocol that uses a neural network of cluster states and local operations, in which input data are encoded through measurement via quantum teleportation. In this design, measurements enable input injection, information processing, and continuous monitoring for time-series tasks. The architecture’s power and versatility are demonstrated through benchmark tasks, showing that the protocol exhibits internal memory and is suitable for both static and temporal information processing without hardware modifications. Additionally, we report a proof-of-principle experimental realization of QRC based on pump-phase encoding and electronic feedback, demonstrating temporal data processing capabilities.
Abstract We propose a new approach for a photonic platform suitable for distributed quantum machine learning and exhibiting memory. This measurement-based quantum reservoir computing takes advantage of continuous variable cluster states as the main quantum resource. Cluster states are key to several photonic quantum technologies, enabling universal quantum computing as well as quantum communication protocols. The proposed measurement-based quantum reservoir computing is based on a neural network of cluster states and local operations, where input data are encoded through measurement, thanks to quantum teleportation. In this design, measurements enable input injections, information processing and continuous monitoring for time series processing. The architecture’s power and versatility are tested by performing a set of benchmark tasks showing that the protocol displays internal memory and is suitable for both static and temporal information processing without hardware modifications. This design opens the way to distributed machine learning.