5 papers
When Does Interference Help Learning? Kernel Geometry as a Pre-Experimental Test for Photonic Reservoir Computing
Oishik Kar, Aswath Babu H
Multi-photon interference is the resource most native to photonic quantum machine learning, yet experiments on photonic reservoirs have reported advantages, null results, and train…
Priority-Aware Routing for Quantum Networks:Integrating Coherence-Time Constraints into Scheduling
Sadhgun Ram Dasi, Aswath Babu H
Quantum networks face a fundamental challenge absent in classical networks; finite memory coherence times mean that queuing delay directly degrades information quality, causing dec…
Explainable Galaxy Interaction Prediction with Hybrid Attention Mechanisms
Sathwik Narkedimilli, Satvik Raghav, Om Mishra +5
Galaxy interaction classification remains challenging due to complex morphological patterns and the limited interpretability of deep learning models. We propose an attentive neural…
A Novel XAI-Enhanced Quantum Adversarial Networks for Velocity Dispersion Modeling in MaNGA Galaxies
Sathwik Narkedimilli, N V Saran Kumar, Aswath Babu H +5
Current quantum machine learning approaches often face challenges balancing predictive accuracy, robustness, and interpretability. To address this, we propose a novel quantum adver…
Comparative Analysis of Black Hole Mass Estimation in Type-2 AGNs: Classical vs. Quantum Machine Learning and Deep Learning Approaches
Sathwik Narkedimilli, Venkata Sriram Amballa, N V Saran Kumar +5
In the case of Type-2 AGNs, estimating the mass of the black hole is challenging. Understanding how galaxies form and evolve requires considerable insight into the mass of black ho…