R. Delhibabu

Machine learning-assisted noise characterization and entanglement verification in superconducting qubits

The scalability of superconducting quantum processors is fundamentally limited by environmental noise and the exponential measurement overhead required for multi-qubit entanglement verification. In this work, we introduce a hybrid machine learning (ML) framework utilizing convolutional neural networks (CNNs)...

BioFirewall: A genome-writing-native governance layer for design-stage biosecurity screening of agentic AI

Background. Artificial-intelligence design tools now plan genome-scale edits, and agentic systems execute those plans with progressively less human oversight. Biosecurity controls are limited to two points: refusal guardrails at the foundation model and sequence-identity screening at the synthesiser. The design stage b...

Reconfigurable trustless digital twins for continuous patient monitoring: A federated learning-blockchain architecture in healthcare 5.0

Echoes of the Runet: uncovering latent socio-political narratives in Russian digital communities using fine-tuned transformer Models

This research demonstrates that applying linguistically native AI models is critical for accurate sociological inference in the Russophone world, bridging the gap between machine learning precision and cultural hermeneutics.

The intersection of post-quantum cryptography and QaaS: architecting quantum-safe cloud infrastructures

A 4-tier security framework tailored for Quantumas-a-Service (QaaS) deployments, focusing on securing data-in-transit, demonstrates the viability of modern latticebased cryptography for live environments and provides a structured 3-phase transition roadmap for cloud service providers (CSPs) to achieve quantum-resilienc...

Hybrid VQA architectures for cloud-based quantum platforms: maximizing computational utility

These quantitative findings demonstrate that tightly coupled hybrid co-processing, physically adjacent to the control electronics, is critical for extending the computational bound of Noisy Intermediate-Scale Quantum (NISQ) devices.

Beyond encryption: post-quantum cryptography and the future of quantum-safe networks

This analysis demonstrates that while hybrid PQC-QKD models reduce long-term key compromise probabilities to near 0%, they introduce a 15% to 40% increase in bandwidth overhead during initial cryptographic handshakes during initial cryptographic handshakes.

Quantum generative AI foundation models: integrating VQAs with fault-tolerant error correction

This paper proposes a hybrid quantum-classical framework utilizing isometric Tree Tensor Networks (TTNs) and a novel Quantum Self-Attention (QSA) subroutine, capable of compressing the latent space of a classical 10-parameter Large Language Model into a 10-parameter quantum neural network via amplitude encoding.

Asynchronous proximal federated aggregation for heterogeneous healthcare networks

The proposed Asynchronous Proximal Federated Aggregation framework effectively mitigates weight divergence, indicating the robustness of asynchronous machine learning for scalable, privacy-preserving clinical diagnostics.

Spatiotemporal vision transformers with Byzantine-robust federated prompt tuning for continuous urban perception

While spatial Vision Transformers (ViTs) achieve high precision in urban scene parsing, their frame-by-frame application in autonomous driving suffers from severe temporal flickering and prohibitive retraining costs across decentralized vehicle fleets. To overcome these dual bottlenecks, this paper introduce...

TeleZK-FL: enabling trustless and verifiable remote patient monitoring via quantized zero-knowledge federated learning

TeleZK-FL establishes the feasibility of verifiable, trustless federated learning on commodity telehealth hardware by eliminating the computational bottlenecks of server-side proof generation while incurring only 0.1%–0.3% AUC degradation.