arXiv subjects

Vinay Kumar

Publications and source records attributed to Vinay Kumar.

At least 19 recordsLinked to original sources

arXiv2026

Photon-efficient quantum repeater chains via hyperentanglement-assisted purification

Linear quantum repeater chains based on Werner-state purification (the BBPSSW protocol) and entanglement swapping (the BDCZ scheme) are fundamental to entanglement distribution in quantum networks. However, they operate under a stringent operation reliability threshold and rely on resource-intensive recurrence purification rounds, each consuming two entangled pairs to probabilistically produce one. In the literature, hyperentanglement has been proposed to exploit multiple degrees of freedom (DOF), such as polarisation and spatial modes, to encode independent entangled states within a single photon pair. This has led to the definition of DAEPP (DOF-Assisted Entanglement Purification Protocol), which we propose to integrate with the BDCZ scheme, resulting in a chain protocol that we call DAHR (DOF-Assisted Hyperentanglement Repeater). The DAEPP step distils the fidelity of a DOF by consuming other(s). In this work, we propose and analyse a DAHR variant which integrates DAEPP at every segment of an end-to-end path combined with BDCZ. We derive a closed-form end-to-end fidelity recursion that embeds single-segment DAEPP into the BDCZ scheme and give a strict resource lower bound for any BDCZ baseline utilising BBPSSW purification to match DAHR's per-segment effective fidelity. At a representative asymmetric operating point informed by prior experiment, we show numerically that matching DAHR's single-photon-pair performance requires two to three rounds of BBPSSW purification. Additionally, below a critical operation reliability, no amount of BBPSSW rounds matches DAHR's one DAEPP round performance.

quant-ph↗

arXiv2026

Making Quantum Networks Work: Routing, Calibration, and Programmable Quantum Repeaters

The quantum internet enables distribution of quantum states across distant nodes, supporting secure communication, distributed computing, and quantum sensing. Unlike classical networks, it is constrained by the no cloning theorem, probabilistic entanglement generation, decoherence, and hardware drift, making classical abstractions inadequate. Scalable quantum networking therefore requires new architectures, protocols, and optimisation methods that explicitly account for these limitations. This thesis studies the architecture, routing, and operation of quantum networks under realistic constraints, focusing on bipartite entanglement distribution over quantum repeater networks. Key metrics include end to end fidelity, throughput, scalability, and fairness. At the network layer, routing strategies are developed beyond assumptions of homogeneous nodes and full network knowledge. Routing under heterogeneous repeater efficiencies shows how partial knowledge of node quality improves fidelity and reduces path blocking. A grey box routing approach is then introduced, where path selection relies only on topology and end to end estimates, achieving robustness and fairness without detailed link information. At the link layer, calibration and hardware drift are addressed through a calibration aware model separating activation and calibration phases. For linear repeater chains, an optimal calibration schedule is derived to balance operation time and calibration overhead. This is extended to general topologies with shared links, where a greedy orchestration heuristic is proposed. Finally, the thesis connects network protocols with hardware via an instruction set architecture for programmable quantum repeater nodes based on NV centers, enabling coherent programmability and linking physical operations to higher layer protocols.

quant-ph↗

arXiv2026

Optimal Calibration of Quantum Network Links

The reliable distribution of entanglement is essential for the effective operation of quantum networks. Due to fundamental differences between quantum and classical communication systems, it is necessary to develop specialised algorithms and protocols that also account for quantum-specific constraints. In this work, we focus on the issue of recalibration. As suggested by recent experimental studies, the process of local entanglement generation in a quantum link degrades over time due to environmental changes that have to be estimated and compensated via a calibration operation, during which the link is not available. Therefore, in such a quantum network, every link alternates between an activation period, during which it operates normally, and a calibration period, during which it cannot participate in the end-to-end entanglement distribution, thereby creating a trade-off between link quality (the fidelity of generated pairs, which decays during activation) and availability (the fraction of time the link is usable, which calibration reduces). We develop analytically a protocol for optimally assigning activation periods to each link in linear quantum repeater chains, subject to any general end-to-end fidelity requirements and local initial fidelity thresholds. Building on this foundation, we extend to general quantum networks, where multiple paths may cross at common links, proposing a heuristic approach evaluated in simulations and compared with a benchmark, numerical approach, and theoretical bounds.

quant-ph↗

arXiv2026

Magnetic Prandtl number dependence of plasmoid-mediated reconnection

We investigate the dependence of the plasmoid-mediated magnetic reconnection rate on the magnetic Prandtl number using two-dimensional magnetohydrodynamic simulations of two coalescing magnetic islands. For Lundquist numbers below the onset of the plasmoid instability, the reconnection rate follows the expected Sweet-Parker scaling and decreases with increasing magnetic Prandtl number. However, once the current sheet becomes plasmoid unstable, the dependence on the magnetic Prandtl number weakens considerably. In the fully plasmoid-mediated regime, we find reconnection rates that remain nearly independent of the magnetic Prandtl number over the explored parameter range. We show that the largest reconnection rates are associated with strongly non-linear phases involving plasmoid interactions and mergers. We further compare our results with simulations of the boundary-driven Taylor problem, where previous studies reported a stronger magnetic Prandtl number dependence, and provide a possible explanation for the differing scalings obtained in the two setups. These results may have implications for reconnection-mediated decay in magnetically dominated turbulence and related astrophysical systems.

physics.plasm-ph↗

arXiv2026

MDGYM: Benchmarking AI Agents on Molecular Simulations

The promise of AI-driven scientific discovery hinges on whether AI agents can autonomously design and execute the computational workflows that underpin modern science. Molecular dynamics (MD) simulation presents a natural test bed to stress-test this claim; it requires translating physical intuition into syntactically and semantically correct input scripts, reasoning about initial and boundary conditions, diagnosing numerically unstable trajectories, and interpreting outputs against known physical behavior and laws. We introduce MDGYM, a benchmark of 169 expert-curated MD simulations spanning LAMMPS and GROMACS, two widely used MD packages, across three increasing difficulty levels. We evaluate three agentic frameworks -- Claude Code, Codex, and OpenHands -- with four LLMs, and find that all perform poorly: even the strongest agent solves only 21\% of easy-level tasks, with less than 10\% at higher difficulties. Trajectory analysis reveals a characteristic pattern of failure -- agents successfully invoke simulation machinery but produce physically unstable configurations, fabricate numerical outputs without executing the underlying computation, or abandon tasks prematurely rather than iterating through simulation-specific errors. These failure modes are qualitatively distinct from those observed in general software engineering benchmarks, indicating that fluent code generation does not transfer to grounded physical reasoning.

cs.AI↗

arXiv2026

Agentic AI Scientists Are Not Built For Autonomous Scientific Discovery

A growing body of work pursues AI scientists capable of end-to-end autonomous scientific discovery. This position paper argues that although they already function as co-scientists, agentic AI scientists are not built for autonomous scientific discovery. We identify the following challenges in building and deploying autonomous AI scientists: (1) Problem selection is influenced by the McNamara fallacy; (2) Agents are built on large language models (LLMs) whose training corpora omit tacit procedural and failure knowledge of laboratory practice; (3) Preference optimisation during post-training compresses output diversity toward consensus; and (4) Most scientific benchmarks measure single-turn prediction accuracy and lack feedback from physical experiments back to the computational model. These challenges are not just questions of scale and scaffolding; they require revisiting fundamental design choices. To build truly autonomous AI scientists, we recommend the use of scientific simulations as verifiers for training, the design of persistent world models that represent the shifting objectives governing real investigations, the establishment of a centralized preregistration repository for all AI-generated hypotheses, and application driven by scientific need rather than tool affordance.

cs.AI↗

arXiv2026

PLACO: A Multi-Stage Framework for Cost-Effective Performance in Human-AI Teams

Human-AI teams play a pivotal role in improving overall system performance when neither the human nor the model can achieve such performance on their own. With the advent of powerful and accessible Generative AI models, several mundane tasks have morphed into Human-AI team tasks. From writing essays to developing advanced algorithms, humans have found that using AI assistance has led to an accelerated work pace like never before. In classification tasks, where the final output is a single hard label, it is crucial to address the combination of human and model output. Prior work elegantly solves this problem using Bayes rule, using the assumption that human and model output are conditionally independent given the ground truth. Specifically, it discusses a combination method to combine a single deterministic labeler (the human) and a probabilistic labeler (the classifier model) using the model's instance-level and the human's class-level calibrated probabilities.

cs.AI↗

arXiv2026

Instruction-Set Architecture for Programmable NV-Center Quantum Repeater Nodes

Programmability is increasingly central in emerging quantum network software stacks, yet the node-internal controller-to-hardware interface for quantum repeater devices remains under-specified. We introduce the idea of an instruction-set architecture (ISA) for controller-driven programmability of nitrogen-vacancy (NV) center quantum repeater nodes. Each node consists of an optically interfaced electron spin acting as a data qubit and a long-lived nuclear-spin register acting as a control program. We formalize two modes of programmability: (i) deterministic register control, where the nuclear register is initialized in a basis state to select a specific operation on the data qubit; and (ii) coherent register control, where the register is prepared in superposition, enabling coherent combinations of operations beyond classical programmability. Network protocols are expressed as controller-issued instruction vectors, which we illustrate through a compact realization of the BBPSSW purification protocol. We further show that coherent register control enables interferometric diagnostics such as fidelity witnessing and calibration, providing tools unavailable in classical programmability. Finally, we discuss scalability to multi-electron and multi-nuclear spin architectures and connection to Linear combination of unitaries (LCU) and Kraus formulation.

quant-ph↗

arXiv2025

PyNoetic: A modular python framework for no-code development of EEG brain-computer interfaces

Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) have emerged as a transformative technology with applications spanning robotics, virtual reality, medicine, and rehabilitation. However, existing BCI frameworks face several limitations, including a lack of stage-wise flexibility essential for experimental research, steep learning curves for researchers without programming expertise, elevated costs due to reliance on proprietary software, and a lack of all-inclusive features leading to the use of multiple external tools affecting research outcomes. To address these challenges, we present PyNoetic, a modular BCI framework designed to cater to the diverse needs of BCI research. PyNoetic is one of the very few frameworks in Python that encompasses the entire BCI design pipeline, from stimulus presentation and data acquisition to channel selection, filtering, feature extraction, artifact removal, and finally simulation and visualization. Notably, PyNoetic introduces an intuitive and end-to-end GUI coupled with a unique pick-and-place configurable flowchart for no-code BCI design, making it accessible to researchers with minimal programming experience. For advanced users, it facilitates the seamless integration of custom functionalities and novel algorithms with minimal coding, ensuring adaptability at each design stage. PyNoetic also includes a rich array of analytical tools such as machine learning models, brain-connectivity indices, systematic testing functionalities via simulation, and evaluation methods of novel paradigms. PyNoetic's strengths lie in its versatility for both offline and real-time BCI development, which streamlines the design process, allowing researchers to focus on more intricate aspects of BCI development and thus accelerate their research endeavors. Project Website: https://neurodiag.github.io/PyNoetic

eess.SP↗

arXiv2025

Uncertainty Quantification and Flow Dynamics in Rotating Detonation Engines

Rotating detonation engines (RDEs) are a critical technology for advancing combustion engines, particularly in applications requiring high efficiency and performance. Understanding the supersonic detonation structure and how various parameters influence these phenomena is essential for optimizing RDE design. In this study, we perform detailed simulations of detonations in an RDE and analyze how the flow patterns are affected by key parameters associated with the droplet arrangement within the engine. To further explore the system's sensitivity, we apply polynomial chaos expansion to investigate the propagation of uncertainties from input parameters to quantities of interest (QOIs). Additionally, we develop a framework to accurately characterize the joint distributions of QOIs with a limited number of simulations. Our findings indicate that the strategic release of droplets may be crucial for sustaining continuous detonation waves in the engine, and accurate representations of the solution (e..g via high-order chaos expansions) are essential to accurately capture the dependence between QOIs and input uncertainties. These insights provide a quantitative foundation for further optimization of RDE designs.

physics.flu-dyn↗

arXiv2025

Quantum Internet: Technologies, Protocols, and Research Challenges

As the field of the quantum internet advances, a comprehensive guide to navigate its complexities has become increasingly crucial. While quantum computing shares foundational principles with the quantum internet, distinguishing between the two is essential for further development and deeper understanding. This work systematically introduces the quantum internet by discussing its importance, core components, operational mechanisms, anticipated timeline for viability, key contributors, major challenges, and future directions. Additionally, it presents the fundamental concepts of quantum mechanics that underpin the technology, offering a clear and targeted overview intended for researchers and industry professionals and laying the groundwork for future innovations and research in the field.

quant-ph↗

arXiv2024

Three-dimensional tearing instability of flux-tube-like magnetic fields

Magnetic reconnection, a fundamental plasma process, is pivotal in understanding energy conversion and particle acceleration in astrophysical systems. While extensively studied in two-dimensional (2D) configurations, the dynamics of reconnection in three-dimensional (3D) systems remain under-explored. In this work, we extend the classical tearing mode instability to 3D by introducing a modulation along the otherwise uniform direction in a 2D equilibrium, given by $g(y)$, mimicking a flux tube-like configuration. We perform linear stability analysis (both analytically and numerically) and direct numerical simulations to investigate the effects of three-dimensionality. Remarkably, we find that a tearing-like instability arises in 3D as well, even without the presence of guide fields. Further, our findings reveal that the 3D tearing instability exhibits reduced growth rates compared to 2D by a factor of $\int g(y)^{1/2} dy~/\int dy$, with the dispersion relation maintaining similar scaling characteristics. We show that the modulation introduces spatially varying resistive layer properties, which influence the reconnection dynamics.

physics.plasm-ph↗

arXiv2024

Exact mean and variance of the squared Hellinger distance for random density matrices

The Hellinger distance between quantum states is a significant measure in quantum information theory, known for its Riemannian and monotonic properties. It is also easier to compute than the Bures distance, another measure that shares these properties. In this work, we derive the mean and variance of the Hellinger distance between pairs of density matrices, where one or both matrices are random. Along the way, we also obtain exact results for the mean affinity and mean square affinity. The first two cumulants of the Hellinger distance allow us to propose an approximation for the corresponding probability density function based on the gamma distribution. Our analytical results are corroborated through Monte Carlo simulations, showing excellent agreement.

quant-ph↗

arXiv2024

Routing in Quantum Networks with End-to-End Knowledge

Given the diverse array of physical systems available for quantum computing and the absence of a well-defined quantum internet protocol stack, the design and optimisation of quantum networking protocols remain largely unexplored. To address this, we introduce an approach that facilitates the establishment of paths capable of delivering end-to-end fidelity above a specified threshold, without requiring detailed knowledge of the quantum network properties. In this study, we define algorithms that are specific instances of this approach and evaluate them in comparison to Dijkstra shortest path algorithm and a fully knowledge-aware algorithm through simulations. Our results demonstrate that one of the proposed algorithms consistently outperforms the other methods in delivering paths above the fidelity threshold, across various network topologies and the number of source-destination pairs involved, while maintaining significant levels of fairness among the users and being robust to inaccurate estimations of the expected end-to-end fidelity.

quant-ph↗

arXiv2024

Closure invariants for polarised radio interferometric observations: a graph theoretical approach

Aperture synthesis observations with full polarisation have long been used to study the magnetic fields of synchrotron emitting sources. Recently proposed closure invariants give us a powerful method for extracting information from measured visibilities which are corrupted by antenna and polarisation dependent gains. In this paper, a formalism developed earlier for complete graphs (where all visibilities are available) is extended to incomplete graphs. The formalism provides a complete and independent set of closure invariants from the measured visibilities in a general situation where not all visibilities are available. We then show in a simulated, quasi-realistic case that the invariants developed here contain usable information even in the presence of noise.

astro-ph.IM↗

arXiv2023

Routing in Quantum Repeater Networks with Mixed Efficiency Figures

This study explores an approach to routing in quantum networks, which targets practical scenarios for quantum networks, mirroring real-world classical networks. By addressing practical constraints, we examine the impact of heterogeneous nodes with mixed efficiency figures on quantum network performance. In particular, we focus on some key parameters in an operational quantum network such as the fraction of nodes with a higher efficiency (called high-quality), path establishment order, end-to-end fidelity, i.e., a measure of the quality of the end-to-end entanglement established. Our simulations show that incorporating knowledge of node quality not only helps boost the fidelity of some of the routing paths but also reduces the number of blocked paths in the quantum network. The study also highlights the critical role of the fraction of high-quality nodes in end-to-end fidelity and explores the trade-offs between upgrading all nodes to high quality or retaining a subset of lower-quality nodes.

quant-ph↗

arXiv2022

Audio-visual video face hallucination with frequency supervision and cross modality support by speech based lip reading loss

Recently, there has been numerous breakthroughs in face hallucination tasks. However, the task remains rather challenging in videos in comparison to the images due to inherent consistency issues. The presence of extra temporal dimension in video face hallucination makes it non-trivial to learn the facial motion through out the sequence. In order to learn these fine spatio-temporal motion details, we propose a novel cross-modal audio-visual Video Face Hallucination Generative Adversarial Network (VFH-GAN). The architecture exploits the semantic correlation of between the movement of the facial structure and the associated speech signal. Another major issue in present video based approaches is the presence of blurriness around the key facial regions such as mouth and lips - where spatial displacement is much higher in comparison to other areas. The proposed approach explicitly defines a lip reading loss to learn the fine grain motion in these facial areas. During training, GANs have potential to fit frequencies from low to high, which leads to miss the hard to synthesize frequencies. Therefore, to add salient frequency features to the network we add a frequency based loss function. The visual and the quantitative comparison with state-of-the-art shows a significant improvement in performance and efficacy.

cs.CV↗

arXiv2022

Variational Approach for Intensity Domain Multi-exposure Image Fusion

Recent innovations shows that blending of details captured by single Low Dynamic Range (LDR) sensor overcomes the limitations of standard digital cameras to capture details from high dynamic range scene. We present a method to produce well-exposed fused image that can be displayed directly on conventional display devices. The ambition is to preserve details in poorly illuminated and brightly illuminated regions. Proposed approach does not require true radiance reconstruction and tone manipulation steps. The aforesaid objective is achieved by taking into account local information measure that select well-exposed regions across input exposures. In addition, Contrast Limited Adaptive Histogram equalization (CLAHE) is introduced to improve uniformity of input multi-exposure image prior to fusion.

cs.CV↗