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.
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.
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.
Continuous-variable (CV) quantum computing offers a promising framework for scalable quantum machine learning, leveraging optical systems with infinite-dimensional Hilbert spaces. While discrete-variable (DV) quantum neural networks have shown remarkable progress in various computer vision tasks, CV quantum models remain comparatively underexplored. In this work, we present a feasibility study of continuous-variable quantum neural networks (CV-QCNNs) applied to biomedical image classification. Utilizing photonic circuit simulation frameworks, we construct CV quantum circuits composed of Gaussian gates, such as displacement, squeezing, rotation, and beamsplitters to emulate convolutional behavior. Our experiments are conducted on the MedMNIST dataset collection, a set of annotated medical image benchmarks for multiple diagnostic tasks. We evaluate CV-QCNN's performance in terms of classification accuracy, model expressiveness, and resilience to Gaussian noise, comparing against classical CNNs and equivalent DV quantum circuits. This study aims to identify trade-offs between DV and CV paradigms for quantum-enhanced medical imaging. Our results highlight the potential of continuous-variable models and their viability for future computer-aided diagnosis systems.
A photonic quantum memory that can store multiple qubits and later recall any randomly chosen subset is essential for large-scale quantum networking and computing. This capability, similar to random-access memory in classical computers, has been difficult to achieve due to the lack of a versatile random-access mechanism and the limited multi-mode storage capacity of existing quantum memory protocols. One promising route uses photon-echo protocols, which apply optical rephasing pulses to selectively read out frequency-multiplexed photonic qubits within a broadened optical transition. Here we advance a photon-echo-based quantum memory protocol using linearly swept adiabatic rephasing pulses, which imprint phase shifts to suppress unwanted echoes. Using the optical transitions of a 171Yb3+:Y2SiO5 crystal, we demonstrate storage and retrieval of multiple complex spectro-temporal photonic modes and achieve random-access memory across eight distinct spectral modes. We show enhanced mode-mapping versatility while substantially lowering the required rephasing pulse intensity, providing a more efficient and reliable approach for high-fidelity qubit storage and retrieval. Scalable quantum networks require memories that store photonic qubits while allowing selective access to individual states, a capability not currently available. A chirped-pulse phase-encoding approach provides random access to spectro-temporal-multiplexed information, enabling storage and retrieval across eight spectral modes with lower rephasing pulse intensity.
Quantum memories for optical states are essential resources for quantum communication and information processing. We propose a quantum memory protocol based on coherent photon-phonon transduction in a Brillouin-active optical waveguide supporting traveling acoustic modes. A pulsed pump drives an effective beam-splitter interaction between optical and acoustic fields, enabling the mapping of a propagating optical quantum state onto a traveling phononic excitation and its subsequent retrieval on demand. Using a continuum optoacoustic model, we show that the protocol enables broadband quantum state storage in a distributed medium without relying on discrete cavity modes. Analytical and numerical results demonstrate high-fidelity storage and retrieval of squeezed and entangled states under experimentally realistic parameters. The memory bandwidth is set by the Brillouin interaction and can reach hundreds of MHz. Our results identify continuum Brillouin optomechanical systems as a scalable platform for broadband quantum memories and multimode quantum signal processing.
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.