arXiv subjects

Xavier Fernando

Publications and source records attributed to Xavier Fernando.

3 recordsLinked to original sources

arXiv2026

Path-Based Quantum Meta-Learning for Adaptive Optimization of Reconfigurable Intelligent Surfaces

Reconfigurable intelligent surfaces (RISs) modify signal reflections to enhance wireless communication capabilities. Classical RIS phase optimization is highly non convex and challenging in dynamic environments due to high interference and user mobility. Here we propose a hierarchical multi-objective quantum metalearning algorithm that switches among specific quantum paths based on historical success, energy cost, and current data rate. Candidate RIS control directions are arranged as switch paths between quantum neural network layers to minimize inference, and a scoring mechanism selects the top performing paths per layer. Instead of merely storing past successful settings of the RIS and picking the closest match when a new problem is encountered, the algorithm learns how to select and recombine the best parts of different solutions to solve new scenarios. In our model, high-dimensional RIS scenario features are compressed into a quantum state using the tensor product, then superimposed during quantum path selection, significantly improving quantum computational advantage. Results demonstrate efficient performance with enhanced spectral efficiency, convergence rate, and adaptability.

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arXiv2026

Quantum Graph Neural Networks for Double-Sided Reconfigurable Intelligent Surface Optimization

As a key enabler for sixth-generation (6G) wireless communications, reconfigurable intelligent surfaces (RISs) provide the flexibility to control signal strength. Nevertheless, optimizing hundreds of elements is computationally expensive. To overcome this challenge, we present a quantum framework (QGCN) to jointly optimize the physical and electromagnetic response of a double-sided RIS design that incorporates discrete phase shifts and inter-element coupling. The core contribution is the adaptive activation or deactivation of elements, allowing a virtual spacing mechanism using PIN diode switches. We then solve a multi-objective problem that maximizes the minimum user data rate subject to constraints on aperture length and mutual coupling between active elements. Experimental results on IBM Quantum's 127-qubit ibm_kyiv superconducting processor demonstrate that the proposed QGCN algorithm reduces both per-iteration computational complexity and memory requirements compared to existing approaches. Also, the QGCN outperforms classical graph neural networks (GNN) on an equivalent graph topology by an additional $+$0.38 bps/Hz. This advantage is increasing with increasing array sizes.

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arXiv2018

Perceptually Inspired Normalized Conditional Compression Distance

Image similarity measurement is a common issue in a broad range of applications in image processing, recognition, classification and retrieval. Conventional image similarity measures are often limited to specific applications and cannot be applied in general scenarios. The theory of Kolmogorov complexity provides a universal framework for a generic similarity metric based on information distance between objects. Normalized Information Distance (NID) has been shown to be a valid and universal distance metric applicable in measurement of similarity of any two objects, and has been successfully applied to a wide range of applications in the past. The difficulty of NID lies in the non-computable nature of the Kolmogorov complexity, and thus approximation has to be applied in practice. Here we propose a perceptually-inspired Normalized Conditional Compression Distance (NCCD) measure by using the Divisive Normalization Transform (DNT) as a means to model the non-linear behavior of the Human Visual System (HVS) in reducing statistical dependencies of visual signals for efficient representation, and show that this perceptual extension of NID can be used in a wide range of image processing applications, including texture classification and face recognition.

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