arXiv subjects

Abhishek Sharma

Publications and source records attributed to Abhishek Sharma.

At least 19 recordsLinked to original sources

arXiv2026

Cat-Code-Protected Controlled Quantum Communication via Non-Local CNOT Gates over Star Quantum Networks

Controlled quantum communication enables secure state transfer between a sender and receiver with the assistance of one or more controllers. However, practical implementation over optical fibre networks is severely hindered by amplitude damping, which reduces fidelity exponentially with distance. We address this challenge by combining two powerful techniques: cat-state encoding for error correction and optimal non-local CNOT gates for distributed gate implementation. The protocol eliminates the need for the physical position qubit itself to travel through the optical fibre, reducing damping events. We show, through density-matrix simulations, that our cat-code-protected protocol with a non-local CNOT operation achieves higher fidelity at 50 km, significantly outperforming the standard protocol. We analyse the protocol's security against beam-splitter attacks and show that CHSH tests provide security against beam-splitter attacks on the distributed entanglement resource despite the cat code's error correction. Our results establish that cat-code-protected controlled quantum communication is feasible with current technology and structurally extensible to multiple controllers, providing a theoretical framework for studying error-corrected controlled quantum communication over long-distance star quantum networks.

quant-ph↗

arXiv2026

FinalityBench: An Effect-Level Benchmark for Agent Decisions Under Delayed and Conflicting Financial Finality

A merchant's payment processor, ledger, ERP and bank feed are updated by messages that get delayed, duplicated, dropped and reordered, so for minutes at a time the four hold contradictory beliefs about the same order. An agent resolving the exception must decide whether to ship goods, re-submit a capture, refund or wait, knowing some of those cannot be undone. We present FinalityBench, an executable benchmark for that decision. It keeps a hidden canonical event log and derives each system's view from a separately faulted delivery stream, so disagreement follows from specified fault semantics rather than being authored. Grading is on executed monetary effects: an episode is scored by the merchant's terminal economic position, relative to a privileged reference told when the pending capture resolves. The corpus of 321 tasks includes 45 twin pairs (90 tasks): tasks whose four system views are identical at the decision instant, whose authoritative probes both return unknown, and whose eventual correct dispositions differ. That snapshot indistinguishability is checked under every evaluation seed rather than assumed; equivalence over all interaction traces is not claimed. Over 14,445 graded episodes from nine programmatic policies, ranking by single-task accuracy and by paired loss disagree in 7 places: a ship-on-first-sign policy is second-best by accuracy at 65.7% and worst in the suite by paired loss, because it cannot tell the two members apart. A runtime gating irreversible actions on an authoritative finality probe reaches 85.4% and, unlike every polling policy, loses nothing to pass^5; its residual loss is almost entirely one archetype, which prices finality information directly. Language models reach the same exact rate as the hand-written gate on a stratified subset, lose about twice as much money, and discover the finality-gating strategy without being told it.

cs.AI↗

arXiv2026

Interpretable statistical feature engineering for early disruption prediction in the short pulse ADITYA tokamak

Reliable early disruption prediction is critical for the safe operation and real-time control of tokamaks. However, machine learning based prediction frameworks have predominantly targeted medium and long pulse devices, with comparatively limited attention given to short pulse tokamaks where available warning time is inherently constrained. In this work, an interpretable machine learning framework is developed for feature engineering and early prediction of disruptions in the ADITYA using the initial plasma evolution information, prior to the activation of the negative converter of the ohmic transformer power supply. Statistical descriptors comprising the mean, variance, skewness, kurtosis and wavelet energy entropy are extracted from routinely available plasma diagnostics over different operation time windows. Decision tree based feature selection is employed to identify physically meaningful disruption precursors and to reduce feature dimensionality. These selected features are used to train a random forest classifier. The proposed framework achieves stable predictive performance across different analysis windows, with a maximum ROC-AUC of 0.87 for 0-35 ms and 0-40 ms windows. Comparable and in some cases improved, performance is obtained using the reduced feature set, demonstrating that the selected statistical descriptors retain the essential information required for disruption prediction. The proposed methodology provides an interpretable and computationally efficient framework for real time disruption prediction in short pulse tokamaks and establishes that carefully engineered statistical descriptors can effectively replace raw time series inputs for early disruption prediction, thereby offering a practical pathway toward real time plasma control in short pulse tokamaks similar to ADITYA and ADITYA-U.

physics.plasm-ph↗

arXiv2026

Domain-Growth Kinetics and Scaling Laws Governing Pulse-Driven Accumulative Polarization Switching in HZO

Accumulative polarization switching driven by sequential sub-coercive electric-field pulses offers a promising route toward low-power ferroelectric memories and neuromorphic devices. However, the kinetic regimes governing this nonequilibrium process remain poorly understood. Here, we employ a phase-field model based on the time-dependent Landau-Ginzburg formalism to investigate pulse-driven accumulative switching in ferroelectric HZO. By systematically varying the initial domain configuration, pulse amplitude, pulse-on time, and pulse-off time, we establish a quantitative link between microscopic domain-wall dynamics and macroscopic polarization accumulation. We show that the effective switched-domain radius follows distinct scaling regimes characterized by the local kinetic exponent. Initially, a local exponent greater than 1 indicates superlinear domain growth driven by enhanced irreversible domain-wall propagation under successive pulses. As switching progresses, a local exponent close to unity marks steady self-similar growth, whereas a local exponent less than 1 signifies decelerating dynamics caused by geometric confinement, depletion of switchable polarization, and relaxation-induced back switching. The transition between these regimes is governed by the competition between field-driven excitation during the pulse-on interval and spontaneous relaxation during the pulse-off interval. The initial domain geometry further influences this transition. Increasing the pulse amplitude or pulse-on duration extends the superlinear regime, whereas longer pulse-off times promote relaxation and suppress accumulation. These findings establish a unified scaling framework for pulse-driven accumulative switching, providing quantitative insight into nonequilibrium ferroelectric domain evolution and design guidelines for HZO-based memory and neuromorphic devices.

cond-mat.mtrl-sci↗

arXiv2026

Elucidating the Size of Chemical Space with Assembly Theory

Chemical space is unimaginably vast with common heuristic estimates suggesting that there are ca. 10^60 'drug-like' molecules possible below a molecular mass of 500 Da. However, these estimates largely ignore the structural and synthetic complexity of the molecules enumerated. Here we present a first-principles estimate of the size of chemical space using the Assembly Theory, which quantifies the amount of causation required to form a molecule, captured in the assembly Index. This is a measurable molecular complexity measure derived from the minimum number of recursive bond-joining operations required to construct a molecular graph. Assembly Theory partitions chemical space into levels defined by Assembly Index, allowing bounds to be placed on its growth as molecular complexity increases. We show that chemical space (the accumulated Assembly Index level sets) grows at least super-exponentially, and at most, double-exponentially with respect to the Assembly Index. Using the GDB-13 database as a reference for growth-rate estimation, we model how chemical space expands under increasing complexity and contracts under structural constraints, including atom and bond types, number of rings, ring size, and chemical motifs. Under constraints comparable to standard drug-like estimates, including molecular mass below 500 Da, our analysis yields a chemical space of approximately 10117 molecules at Assembly Index 25. Finally, we constrain chemical space by biologically relevant motifs and identify structurally relevant molecules near the accessible boundaries of these assembly-defined spaces.

physics.chem-ph↗

arXiv2026

BADGER: Bridging Agentic and Deterministic Evaluation for Generative Enterprise Reasoning

Enterprise AI systems that translate natural language into SQL queries and orchestrate multi-step agentic reasoning pipelines require evaluation approaches fundamentally different from academic benchmarks. Spider and BIRD established execution-accuracy protocols; G-Eval and RAGAS advanced LLM-based assessment; and recent work such as Spider 2.0, BEAVER, and BIRD-Interact has begun to address enterprise and agentic dimensions. No single framework unifies text-to-SQL assessment with agentic behavior evaluation into a production-grade pipeline calibrated against human expert judgment. We present BADGER, developed at Merkle, a unified evaluation framework integrating text-to-SQL assessment with agentic behavior evaluation. BADGER offers three contributions. First, LLM-assisted SQL component extraction extending Spider methodology to handle CTE-heavy, dialect-specific SQL. Second, a hybrid execution accuracy metric (Hybrid-EX) resolving column-aliasing and numeric-tolerance brittleness by using an LLM to infer structural alignments before deterministic cell-level scoring. Validated on 150 human-annotated industry queries, Hybrid-EX achieves Cohen's kappa=0.717 [95% CI: 0.600-0.822] (Substantial agreement) and 87.3% balanced accuracy, outperforming all six competing frameworks (Delta-kappa: 0.322-0.502, all p<=0.001). Third, an enterprise agentic evaluation suite assembling RAGAS, G-Eval, and agent benchmark metrics into a unified pipeline; Excess Tool Usage is the sole novel element. BADGER runs entirely within the client's governed data environment, supports configurable LLM judge backends, and enables rapid prototyping of client-specific judges and metrics, serving as a continuous evaluation backbone rather than a one-time quality gate.

cs.AI↗

arXiv2026

Quantifying Potential Observation Missingness in Inverse Reinforcement Learning

Inverse reinforcement learning (IRL), which infers reward functions from demonstrations, is a valuable tool for modeling and understanding decision-making behavior. Many variants of IRL have been developed to capture complexities of human decision-making, such as subjective beliefs, imperfect planning, and dynamic goals. However, an often-overlooked issue in real-world behavioral datasets is that the recorded data may be missing observations that were available to the original decision-maker. In use-inspired settings such as healthcare, this can make expert actions appear suboptimal, even when they were near-optimal given the information available at the time. As a result, the rewards learned by standard IRL may be misleading. In this paper, we identify the minimal perturbations to the recorded observations needed for the expert's actions to appear optimal. We develop a practical algorithm for this problem and demonstrate its utility for quantifying the possible extent of missing observations in behavioral datasets through extensive experiments on synthetic navigation tasks, a cancer treatment simulator, and ICU treatment data.

cs.LG↗

arXiv2026

Flocking through a sea of rods

We investigate the collective behavior of motile rods immersed in a monolayer of apolar rods confined between vertically vibrating plates using numerical simulations. We uncover an antidiffusive instability whereby motile rods segregate from the apolar medium and form flocks whose size increases with the medium concentration. Remarkably, enhanced segregation leads to a reduction of the global polar order. The flock structure is strongly influenced by the anisotropy of the medium rods. For small aspect ratios, the flocks are elongated perpendicular to the mean direction of motion, whereas for larger aspect ratios, they elongate along the direction of motility. We rationalize the emergence of segregation-induced disorder using a minimal mean-field model.

cond-mat.soft↗

arXiv2026

Development of Low-Mass Flex PCB and Nanowire Interconnect Technologies for HEP Module Integration

The development of lightweight flex PCBs and nanowire-based thermal interfaces for low-mass, high-performance detector modules are presented. A novel manufacturing approach targeting flex circuits with double-sided pad access, assembled using ACF and gold studs. Signal integrity was simulated and validation trials conducted on test structures. For thermal management, sintered nanowire interfaces were evaluated. These results contribute quantitative input relevant to minimal-mass, scalable packaging in HEP detectors.

physics.ins-det↗

arXiv2026

IndIGO-D: Probing Compact Binary Coalescences in the Decihertz GW Band

We study IndIGO-D, a decihertz gravitational-wave mission concept, focusing on a specific configuration in which three spacecraft fly in formation to form an L-shaped interferometer in a heliocentric orbit. The two orthogonal arms share a common vertex, providing a space-based analogue of terrestrial Michelson detectors, while operating in an optimised configuration that yields ppm-level arm-length stability. Assuming 1000 km arm length, we analyse the orbital motion and antenna response, and assess sensitivity across the [0.1 - 10] Hz band bridging LISA and next-generation ground-based interferometers. Using fiducial sensitivity curves provided by the IndIGO-D collaboration, we compute horizon distances for different source classes. Intermediate-mass black-hole binaries with masses $10^{2}$ - $10^{3} \, M_\odot$ are detectable to redshifts $z \sim 10^{3}$, complementing the reach of LISA and terrestrial detectors. Binary neutron star systems are observable to a horizon distance of $z \lesssim 0.3$, allowing continuous multi-band coverage with Voyager-class interferometers from the decihertz regime to merger. A Bayesian parameter-estimation study of a GW170817-like binary shows that the sky localization area improves from $\sim 21 \,\mathrm{deg}^2$ at one month to $0.3 \,\mathrm{deg}^2$ at six hours pre-merger! These sky areas are readily tiled by wide-field time-domain telescopes such as the Rubin Observatory, whose $9.6 \,\mathrm{deg}^2$ field of view and r-band depth enable high-cadence, repeated coverage of GW170817-like kilonovae at this distance and beyond. IndIGO-D exploits the rapid evolution of binaries in the decihertz band to bridge the gap between millihertz and terrestrial observations, enabling early warnings on timescales from months to hours and enhancing the prospects for multi-band and multi-messenger discoveries.

gr-qc↗

arXiv2026

Image Synthesis Using Spintronic Deep Convolutional Generative Adversarial Network

The computational requirements of generative adversarial networks (GANs) exceed the limit of conventional Von Neumann architectures, necessitating energy efficient alternatives such as neuromorphic spintronics. This work presents a hybrid CMOS-spintronic deep convolutional generative adversarial network (DCGAN) architecture for synthetic image generation. The proposed generative vision model approach follows the standard framework, leveraging generator and discriminators adversarial training with our designed spintronics hardware for deconvolution, convolution, and activation layers of the DCGAN architecture. To enable hardware aware spintronic implementation, the generator's deconvolution layers are restructured as zero padded convolution, allowing seamless integration with a 6-bit skyrmion based synapse in a crossbar, without compromising training performance. Nonlinear activation functions are implemented using a hybrid CMOS domain wall based Rectified linear unit (ReLU) and Leaky ReLU units. Our proposed tunable Leaky ReLU employs domain wall position coded, continuous resistance states and a piecewise uniaxial parabolic anisotropy profile with a parallel MTJ readout, exhibiting energy consumption of 0.192 pJ. Our spintronic DCGAN model demonstrates adaptability across both grayscale and colored datasets, achieving Fr'echet Inception Distances (FID) of 27.5 for the Fashion MNIST and 45.4 for Anime Face datasets, with testing energy (training energy) of 4.9 nJ (14.97~nJ/image) and 24.72 nJ (74.7 nJ/image).

physics.app-ph↗

arXiv2025

Tunable Magnetic and Topological Phases in EuMnXBi$_2$ (X=Mn, Fe, Co, Zn) Pnictides

We present a comprehensive density functional theory (DFT) study of the electronic, magnetic, and topological properties of the layered pnictides EuMnXBi2 (X = Mn, Fe, Co, Zn), focusing in particular on the relatively unexplored Bi-based member of the EuMn2X2 family. Unlike the well-studied As-, Sb-, and P--based analogues, we show that EuMn2Bi2 stabilizes in a C-type antiferromagnetic ground state with a narrow-gap semiconducting character. Inclusion of spin-orbit coupling (SOC) drives a transition from this trivial antiferromagnetic semiconductor to a Weyl semimetal hosting four symmetry-related Weyl points and robust Fermi arc states. Systematic substitution of Mn with Fe, Co, and Zn further reveals a tunable sequence of magnetic ground states: Fe and Co induce ferrimagnetism with semimetallic behavior, while Zn stabilizes a ferromagnetic semimetal with a large net moment. These findings establish Bi-based EuMnXBi2 pnictides as a versatile platform where magnetic exchange interactions and band topology can be engineered through SOC and chemical substitution. The complex interplay of magnetic interactions and topological effects in the proposed bulk and doped pnictides opens a promising avenue to explore a wide range of electronic and magnetic phenomena. In particular, this study demonstrates that EuMn2Bi2 hosts tunable magnetic and topological phases driven by electron correlations, chemical substitution, and spin-orbit coupling.

cond-mat.mtrl-sci↗

arXiv2025

Quantifying the Emergence of Selection Prior to Biological Evolution

Selection is central to biological evolution, yet there has been no general experimental framework for quantifying selection in chemical systems before life. Here we demonstrate that selection in a prebiological chemical system can be directly quantified. Assembly Theory predicts that selection corresponds to a transition from undirected to directed exploration of chemical possibility space, measurable through the amount of Assembly, A, which integrates molecular assembly index with observed copy number. By analysing peptide ensembles produced under diverse polymerisation conditions, we show that undirected reactions explore sequence space almost uniformly, yielding exploration ratios of 0.85-0.95, whereas reactions influenced by evolved proteases generate markedly lower ratios (0.51-0.75) and elevated A, consistent with selective reinforcement of specific assembly pathways. Across multiple environments and amino-acid combinations, the exploration ratio and ensemble assembly A robustly distinguish directed from undirected exploration, establishing a general, experimentally tractable metric for detecting and measuring selection in chemical evolution.

q-bio.MN↗

arXiv2025

Dynamic Lagging for Time-Series Forecasting in E-Commerce Finance: Mitigating Information Loss with A Hybrid ML Architecture

Accurate forecasting in the e-commerce finance domain is particularly challenging due to irregular invoice schedules, payment deferrals, and user-specific behavioral variability. These factors, combined with sparse datasets and short historical windows, limit the effectiveness of conventional time-series methods. While deep learning and Transformer-based models have shown promise in other domains, their performance deteriorates under partial observability and limited historical data. To address these challenges, we propose a hybrid forecasting framework that integrates dynamic lagged feature engineering and adaptive rolling-window representations with classical statistical models and ensemble learners. Our approach explicitly incorporates invoice-level behavioral modeling, structured lag of support data, and custom stability-aware loss functions, enabling robust forecasts in sparse and irregular financial settings. Empirical results demonstrate an approximate 5% reduction in MAPE compared to baseline models, translating into substantial financial savings. Furthermore, the framework enhances forecast stability over quarterly horizons and strengthens feature target correlation by capturing both short- and long-term patterns, leveraging user profile attributes, and simulating upcoming invoice behaviors. These findings underscore the value of combining structured lagging, invoice-level closure modeling, and behavioral insights to advance predictive accuracy in sparse financial time-series forecasting.

cs.LG↗

arXiv2025

Rapid parameter estimation with the full symphony of compact binary mergers using meshfree approximation

We present a fast Bayesian inference framework to address the growing computational cost of gravitational-wave parameter estimation. The increased cost is driven by improved broadband detector sensitivity, particularly at low frequencies due to advances in detector commissioning, resulting in longer in-band signals and a higher detection rate. Waveform models now incorporate features like higher-order modes, further increasing the complexity of standard inference methods. Our framework employs meshfree likelihood interpolation with radial basis functions to accelerate Bayesian inference using the IMRPhenomXHM waveform model that incorporates higher modes of the gravitational-wave signal. In the initial start-up stage, interpolation nodes are placed within a constant-match metric ellipsoid in the intrinsic parameter space. During sampling, likelihood is evaluated directly using the precomputed interpolants, bypassing the costly steps of on-the-fly waveform generation and overlap-integral computation. We improve efficiency by sampling in a rotated parameter space aligned with the eigenbasis of the metric ellipsoid, where parameters are uncorrelated by construction. This speeds up sampler convergence. This method yields unbiased parameter recovery when applied to 100 simulated neutron-star-black-hole signals (NSBH) in LIGO-Virgo data, while reducing computational cost by up to an order of magnitude for the longest-duration signal. The meshfree framework equally applies to symmetric compact binary systems dominated by the quadrupole mode, supporting parameter estimation across a broad range of sources. Applied to a simulated NSBH signal in Einstein Telescope data, where the effects of Earth's rotation are neglected for simplicity, our method achieves an O(10^4) speed-up, demonstrating its potential use in the third-generation (3G) era.

gr-qc↗

arXiv2025

Exploring molecular assembly as a biosignature using mass spectrometry and machine learning

Molecular assembly offers a promising path to detect life beyond Earth, while minimizing assumptions based on terrestrial life. As mass spectrometers will be central to upcoming Solar System missions, predicting molecular assembly from their data without needing to elucidate unknown structures will be essential for unbiased life detection. An ideal agnostic biosignature must be interpretable and experimentally measurable. Here, we show that molecular assembly, a recently developed approach to measure objects that have been produced by evolution, satisfies both criteria. First, it is interpretable for life detection, as it reflects the assembly of molecules with their bonds as building blocks, in contrast to approaches that discount construction history. Second, it can be determined without structural elucidation, as it can be physically measured by mass spectrometry, a property that distinguishes it from other approaches that use structure-based information measures for molecular complexity. Whilst molecular assembly is directly measurable using mass spectrometry data, there are limits imposed by mission constraints. To address this, we developed a machine learning model that predicts molecular assembly with high accuracy, reducing error by three-fold compared to baseline models. Simulated data shows that even small instrumental inconsistencies can double model error, emphasizing the need for standardization. These results suggest that standardized mass spectrometry databases could enable accurate molecular assembly prediction, without structural elucidation, providing a proof-of-concept for future astrobiology missions.

cs.LG↗

arXiv2025

Finite size effect in Kuramoto oscillators with inertia on simplicial complex

We investigate the finite-size effects on the dynamical evolution of the Kuramoto model with inertia coupled through triadic interactions. Our findings reveal that fluctuations resulting from the finite size drive the system toward a synchronized state at finite coupling, which contrasts with the analytical predictions {in thermodynamic limit} made for the same system. Building on the analytical calculations performed at the thermodynamic limit, we identify the origin of the synchronization transition that arises because of the finite size. We discover a power-law relationship between the network size and the critical coupling at which the first-order transition to synchronization occurs. Additionally, as inertia increases, there is a significant shift in the critical coupling toward higher values, indicating that inertia counteracts the effects caused by finite size.

nlin.AO↗

arXiv2025

Frictional Contact Network in Dense Suspension Flow

Dense particulate suspensions often exhibit a dramatic increase in viscosity in response to external deformation. This shear thickening behavior has been related to a transition from lubricated, unconstrained pairwise motion to a frictional contact network (FCN) at high stresses. Here, we study the characteristics of the FCN formed during shear thickening to investigate the role of constraints, emphasizing the impact of resistance to gear-like rolling. We contrast the FCN formed by sliding friction alone with that formed by particles with sliding and rolling constraints. Particles with sliding constraints only form a highly interconnected network with primary force chains in the compressive direction, which requires orthogonal support from other force chains. However, orthogonal support is not required for mechanical stability when particles have both sliding and rolling constraints. In addition, the force chains appear linear and longer, reducing the jamming volume fraction for rough/faceted particles. Finally, we propose a novel mechanical stability picture for rough/faceted particles with sliding and rolling constraints, which is crucial for understanding the flow behavior of real-life suspensions.

cond-mat.soft↗