Radhakrishnan Delhibabu

Joint optimisation of amino acid and coding sequence for de novo designed proteins

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

Hybrid Variational Quantum Algorithms (VQAs) present a highly viable pathway to near-term quantum utility; however, their performance is fundamentally bottlenecked by classical-quantum communication latency in Quantumas-a-Service (QaaS) environments. This paper proposes an optimized classical-quantum orchestration architecture designed to minimize cloud-induced latency and maximize Quantum Processing Unit (QPU) active compute time. By implementing edge-colocated classical optimizers alongside batched parameter-shift gradient evaluations, the system circumvents stateless cloud API barriers. Benchmarking across parameterized quantum circuits ranging from 15 to 50 qubits demonstrates an 84% reduction in network-induced QPU idle time. The framework yields a 3.2 × speedup in overall convergence time for the Quantum Approximate Optimization Algorithm (QAOA) and up to a 98% reduction in classical API call overhead compared to standard RESTful QaaS execution models. 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.

JANUS: data and reproducibility artefacts

Derived data, run outputs and figure source data supporting the article "Joint optimisation of amino acid and coding sequence for de novo designed proteins". Includes the inverse-folding marginals for all 862 backbones and the full 230,992-row double-mutant additivity table.

JANUS: data and reproducibility artefacts

Derived data, run outputs and figure source data supporting the article "Joint optimisation of amino acid and coding sequence for de novo designed proteins". Includes the inverse-folding marginals for all 862 backbones and the full 230,992-row double-mutant additivity table.

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

Introduction 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. Methods To overcome these dual bottlenecks, this paper introduces a unified Spatiotemporal Hierarchical Mask-Refinement (ST-HMR) framework integrated with a Byzantine-Robust Federated Learning (BR-FL) protocol. The ST-HMR module caches fine-grained prompt tokens via an asymmetrical Temporal Cross- Attention buffer to enforce inter-frame geometric continuity. Concurrently, the BR-FL pipeline employs Multi-Krum geometric distance filtration to aggregate 3 localized prompt gradients from decentralized fleets securely, updating only a 1.4% active parameter subset. Results Evaluated on the Cityscapes Video dataset, the ST-HMR framework improves the video segmentation mean Intersection over Union (mIoU) to 83.5%, elevates the Temporal Consistency (TC) score to 88.5, and reduces depth Absolute Relative Error (Abs Rel) to 0.085, all while maintaining real-time edge processing at 38 FPS. Under severe adversarial network conditions (up to 30% Byzantine/malicious sensor nodes), the BR-FL protocol achieves a 98.4% Byzantine detection rate and maintains a global mIoU of 81.9%, while reducing Over-The-Air (OTA) transmission payloads by over 99% (3.8 MB vs. 1.2 GB per round). Discussion These findings demonstrate that parameter-efficient prompt caching eliminates temporal boundary jitter without heavy 3D transformer overhead, while geometric gradient filtering provides robust defense against decentralized poisoning, establishing a scalable, secure, and temporally coherent perception paradigm for next-generation edge robotics.