QFGN: A Quantum Approach to High-Fidelity Implicit Neural Representations

Implicit neural representations have shown potential in various applications. However, accurately reconstructing the image or providing clear details via image super-resolution remains challenging. This paper introduces Quantum Fourier Gaussian Network (QFGN), a quantum-based machine learning model for better signal representations. The frequency spectrum is well balanced by penalizing the low-frequency components, leading to the improved expressivity of quantum circuits. The results demonstrate that with minimal parameters, QFGN outperforms the current state-of-the-art (SOTA) models. Despite noise on hardware, the model achieves accuracy comparable to that of SIREN, highlighting the potential applications of quantum machine learning in this field.

Benchmarking MedMNIST dataset on real quantum hardware

Quantum machine learning (QML) has emerged as a promising domain to leverage the computational capabilities of quantum systems to solve complex classification tasks. In this work, we present first comprehensive QML study by benchmarking the MedMNIST-a diverse collection of medical imaging datasets on a 127-qubit real IBM quantum hardware, to evaluate the feasibility and performance of quantum models (without any classical neural networks) in practical applications. This study explore recent advancements in quantum computing such as device-aware quantum circuits, error suppression and mitigation for medical image classification. Our methodology comprised of three stages: preprocessing, generation of noise-resilient and hardware-efficient quantum circuits, optimizing/training of quantum circuits on classical hardware, and inference on real IBM quantum hardware. Firstly, we process all input images in the preprocessing stage to reduce the spatial dimension due to the quantum hardware limitations. We generate hardware-efficient quantum circuits using backend properties expressible to learn complex patterns for medical image classification. After classical optimization of QML models, we perform the inference on real quantum hardware. We also incorporates advanced error suppression and mitigation techniques in our QML workflow including dynamical decoupling (DD), gate twirling, and matrix-free measurement mitigation (M3) to mitigate the effects of noise and improve classification performance. The experimental results showcase the potential of quantum computing for medical imaging and establishes a benchmark for future advancements in QML applied to healthcare.

Towards 6G-V2X: Hybrid RF-VLC for Vehicular Networks

With the advent of connected autonomous vehicles, we are expecting to witness a new era of unprecedented user experiences, improved road safety, a wide range of compelling transportation applications, etc. A large number of disruptive communication technologies are emerging for the sixth generation (6G) wireless network aiming to support advanced use cases for intelligent transportation systems (ITS). An example of such a disruptive technology is constituted by hybrid Visible Light Communication (VLC) and Radio Frequency (RF) systems, which can play a major role in advanced ITS. Hence we outline the potential benefits of hybrid vehicular-VLC (V-VLC) and vehicular-RF (V-RF) communication systems over standalone V-VLC and standalone V-RF systems. In particular, we show that the link-aggregated hybrid V-VLC/V-RF system is capable of meeting stringent ultra high reliability (~99.999%) and ultra-low latency (<3 ms) specifications, making it a promising candidate for 6G ITS. To stimulate future research in the hybrid RF-VLC V2X area, we also highlight the potential challenges and research directions.