arXiv subjects

Tathagata Banerjee

Publications and source records attributed to Tathagata Banerjee.

At least 19 recordsLinked to original sources

arXiv2026

Resist-free shadow deposition using silicon trenches for Josephson junctions in superconducting qubits

Superconducting qubit fabrication innovations continue to be explored to achieve higher performance. Despite improvements to base layer fabrication and processing, resist-based Josephson junction (JJ) schemes have largely remained unchanged. The polymer mask during deposition causes chemical contamination and limits in situ and ex situ surface preparation, junction materials, and scalability. Here, we demonstrate a resist-free approach to junction fabrication based on etched silicon trenches that is CMOS compatible and easily integrated into existing innovations in qubit base layer fabrication and chemical processing. We fabricate Al-AlOx-Al JJs and qubits using this method, measuring median energy relaxation times up to 184 microseconds. We find minimal contamination at the substrate-metal interface and fluctuations of energy relaxation on a 35 hour timescale that are narrow and normally distributed. The method widens the process window for substrate preparation and new materials platforms.

quant-ph↗

arXiv2026

Fabrication effects on Niobium oxidation and surface contamination in Niobium-metal bilayers using X-ray photoelectron spectroscopy

Superconducting resonators and qubits are limited by dielectric losses from surface oxides. Surface oxides are mitigated through various strategies such as the addition of a metal capping layer, surface passivation, and acid processing. In this study, we demonstrate the use of X-ray photoelectron spectroscopy (XPS) as a rapid characterization tool to study the effectiveness cap layers for niobium for further device fabrication. We non-destructively evaluate 17 capping layers to characterize their ability to prevent oxygen diffusion, and the effects of standard fabrication processes -- annealing, resist stripping, and acid cleaning. We downselect for resilient capping layers and test their microwave resonator performance.

cond-mat.mtrl-sci↗

arXiv2025

Adapting Skill Ratings to Luck-Based Hidden-Information Games

Rating systems play a crucial role in evaluating player skill across competitive environments. The Elo rating system, originally designed for deterministic and information-complete games such as chess, has been widely adopted and modified in various domains. However, the traditional Elo rating system only considers game outcomes for rating calculation and assumes uniform initial states across players. This raises important methodological challenges in skill modelling for popular partially randomized incomplete-information games such as Rummy. In this paper, we examine the limitations of conventional Elo ratings when applied to luck-driven environments and propose a modified Elo framework specifically tailored for Rummy. Our approach incorporates score-based performance metrics and explicitly models the influence of initial hand quality to disentangle skill from luck. Through extensive simulations involving 270,000 games across six strategies of varying sophistication, we demonstrate that our proposed system achieves stable convergence, superior discriminative power, and enhanced predictive accuracy compared to traditional Elo formulations. The framework maintains computational simplicity while effectively capturing the interplay of skill, strategy, and randomness, with broad applicability to other stochastic competitive environments.

cs.GT↗

arXiv2025

Empirical parameterization of the Elo Rating System

This study aims to provide a data-driven approach for empirically tuning and validating rating systems, focusing on the Elo system. Well-known rating frameworks, such as Elo, Glicko, TrueSkill systems, rely on parameters that are usually chosen based on probabilistic assumptions or conventions, and do not utilize game-specific data. To address this issue, we propose a methodology that learns optimal parameter values by maximizing the predictive accuracy of match outcomes. The proposed parameter-tuning framework is a generalizable method that can be extended to any rating system, even for multiplayer setups, through suitable modification of the parameter space. Implementation of the rating system on real and simulated gameplay data demonstrates the suitability of the data-driven rating system in modeling player performance.

stat.AP↗

arXiv2025

Active-Learning Inspired $\textit{Ab Initio}$ Theory-Experiment Loop Approach for Management of Material Defects: Application to Superconducting Qubits

Surface oxides are associated with two-level systems (TLSs) that degrade the performance of niobium-based superconducting quantum computing devices. To address this, we introduce a predictive framework for selecting metal capping layers that inhibit niobium oxide formation. Using DFT-calculated oxygen interstitial and vacancy energies as thermodynamic descriptors, we train a logistic regression model on a limited set of experimental outcomes to successfully predict the likelihood of oxide formation beneath different capping materials. This approach identifies Zr, Hf, and Ta as effective diffusion barriers. Our analysis further reveals that the oxide formation energy per oxygen atom serves as an excellent standalone descriptor for predicting barrier performance. By combining this new descriptor with lattice mismatch as a secondary criterion to promote structurally coherent interfaces, we identify Zr, Ta, and Sc as especially promising candidates. This closed-loop strategy integrates first-principles theory, machine learning, and limited experimental data to enable rational design of next-generation materials.

cond-mat.mtrl-sci↗

arXiv2025

Low barrier ZrO$_x$-based Josephson junctions

The Josephson junction is a crucial element in superconducting devices, and niobium is a promising candidate for the superconducting material due to its large energy gap relative to aluminum. AlO$_x$ has long been regarded as the highest quality oxide tunnel barrier and is often used in niobium-based junctions. Here we propose ZrO$_x$ as an alternative tunnel barrier material for Nb electrodes. We theoretically estimate that zirconium oxide has excellent oxygen retention properties and experimentally verify that there is no significant oxygen diffusion leading to NbO$_x$ formation in the adjacent Nb electrode. We develop a top-down, subtractive fabrication process for Nb/Zr-ZrO$_x$/Nb Josephson junctions, which enables scalability and large-scale production of superconducting electronics. Using cross sectional scanning transmission electron microscopy, we experimentally find that depending on the Zr thickness, ZrO$_x$ tunnel barriers can be fully crystalline with chemically abrupt interfaces with niobium. Further analysis using electron energy loss spectroscopy reveals that ZrO$_x$ corresponds to tetragonal ZrO$_2$. Room temperature characterization of fabricated junctions using Simmons' model shows that ZrO$_2$ exhibits a low tunnel barrier height, which is promising in merged-element transmon applications. Low temperature transport measurements reveal sub-gap structure, while the low-voltage sub-gap resistance remains in the megaohm range.

cond-mat.supr-con↗

arXiv2025

Skill vs. Chance Quantification for Popular Card & Board Games

This paper presents a data-driven statistical framework to quantify the role of skill in games, addressing the long-standing question of whether success in a game is predominantly driven by skill or chance. We analyze player level data from four popular games Chess, Rummy, Ludo, and Teen Patti, using empirical win statistics across varying levels of experience. By modeling win rate as a function of experience through a regression framework and employing empirical bootstrap resampling, we estimate the degree to which outcomes improve with repeated play. To summarize these dynamics, we propose a flexible skill score that emphasizes learning over initial performance, aligning with practical and regulatory interpretations of skill. Our results reveal a clear ranking, with Chess showing the highest skill component and Teen Patti the lowest, while Rummy and Ludo fall in between. The proposed framework is transparent, reproducible, and adaptable to other game formats and outcome metrics, offering potential applications in legal classification, game design, and player performance analysis.

cs.GT↗

arXiv2025

Low-loss Nb on Si superconducting resonators from a dual-use spintronics deposition chamber and with acid-free post-processing

Magnetic impurities are known to degrade superconductivity. For this reason, physical vapor deposition chambers that have previously been used for magnetic materials have generally been avoided for making high-quality superconducting resonator devices. In this article, we show by example that such chambers can be used for this purpose; with Nb films sputtered in a chamber that continues to be used for magnetic materials, we demonstrate compact (\SI{3}{\micro\meter} gap) coplanar waveguide resonators with low-power internal quality factors near one million. We achieve this using a resist strip bath with no post-fabrication acid treatment, which results in performance comparable to previous strip baths with acid treatments. We also find evidence that this improved resist strip bath provides a better surface chemical template for post-fabrication hydrogen fluoride processing. These results are consistent across three Si substrate preparation methods, including a \SI{700}{\celsius} anneal. These results will inform nanofabrication for other superconducting materials and the integration of magnetic materials for hybrid systems.

quant-ph↗

arXiv2024

Molecular beam epitaxy growth of superconducting tantalum germanide

Developing new material platforms for use in superconductor-semiconductor hybrid structures is desirable due to limitations caused by intrinsic microwave losses present in commonly used III/V material systems. With the recent reports on tantalum superconducting qubits that show improvements over the Nb and Al counterparts, exploring Ta as an alternative superconductor in hybrid material systems is promising. Here, we study the growth of Ta on semiconducting Ge (001) substrates grown via molecular beam epitaxy. We show that at a growth temperature of 400$^{\circ}$C the Ta diffuses into the Ge matrix in a self-limiting nature resulting in smooth and abrupt surfaces and interfaces with roughness on the order of 3-7 Å as measured by atomic force microscopy and x-ray reflectivity. The films are found to be a mixture of Ta$_{5}$Ge$_{3}$ and TaGe$_{2}$ binary alloys and form a native oxide that seems to form a sharp interface with the underlying film. These films are superconducting with a $T_{C}\sim 1.8-2$K and $H_{C}^{\perp} \sim 1.88T$, $H_{C}^{\parallel} \sim 5.1T$. These results show this tantalum germanide film to be promising for future superconducting quantum information platforms.

cond-mat.mes-hall↗

arXiv2024

Skill Dominance Analysis of Two(Four) player, Three(Five) dice Variant of the Ludo Game

This paper examines two different variants of the Ludo game, involving multiple dice and a fixed number of total turns. Within each variant, multiple game lengths (total no. of turns) are considered. To compare the two variants, a set of intuitive, rule-based strategies is designed, representing different broad methods of strategic play. Game play is simulated between bots (automated software applications executing repetitive tasks over a network) following these strategies. The expected results are computed using certain game theoretic and probabilistic explanations, helping to understand the performance of the different strategies. The different strategies are further analyzed using win percentage in a large number of simulations, and Nash Equilibrium strategies are computed for both variants for a varying number of total turns. The Nash Equilibrium strategies across different game lengths are compared. A clear distinction between performances of strategies is observed, with more sophisticated strategies beating the naive one. A gradual shift in optimal strategy profiles is observed with changing game length, and certain sophisticated strategies even confound each other's performance while playing against each other.

cs.GT↗

arXiv2024

Re-investigation of the interplay of fission modes and non-equilibrium fission processes in heavy actinide nuclei 249Bk and 257Md

Measurements of mass and angular distributions of fission fragments from actinide nuclei 249Bk and 257Md, produced in fusion reactions 11B and 19F + 238238U, are presented. Experimentally observed mass ratio distributions indicate 'multi-chance fission' through the interplay of fission modes in the fission process, and they agree well with predictions from calculations using the GEF ('GEneral description of Fission observables') model code. Furthermore, to test the signatures of events from non-compound nuclear processes in the fission of 249Bk and 257Md nuclei, Monte Carlo statistical decay model calculations using GEMINI++ were performed for the measured mass distribution at all energies. For comparison purposes, the fission fragment mass distributions of neighboring heavy actinide nuclei, previously measured in the fission of 250Cf and 254Fm nuclei produced by 12C and 16O projectiles on a 238U target, are also presented. The measured angular anisotropy data for the 19F + 238U reaction differ from the results of the Transition State Model at energies below the fusion barrier. As a result of the present study, we suggest considering the interplay between K relaxation time, dynamic dissipation, and their influence on shell correction to understand the evolution of fission dynamics in heavy-ion-induced actinide nuclei

nucl-ex↗

arXiv2024

Dynamic Clearing and Contagion in Financial Networks

In this paper we introduce a generalized extension of the Eisenberg-Noe model of financial contagion to allow for time dynamics of the interbank liabilities, including a dynamic examination of default risk. This framework separates the cash account and long-term capital account to more accurately model the health of a financial institution. In doing so, such a system allows us to distinguish between delinquency and default as well as between defaults resulting from either insolvency or illiquidity.

q-fin.MF↗

arXiv2021

$C^*$-extreme points of positive operator valued measures and unital completely positive maps

We study the quantum ($C^*$) convexity structure of normalized positive operator valued measures (POVMs) on measurable spaces. In particular, it is seen that unlike extreme points under classical convexity, $C^*$-extreme points of normalized POVMs on countable spaces (in particular for finite sets) are always spectral measures (normalized projection valued measures). More generally it is shown that atomic $C^*$-extreme points are spectral. A Krein-Milman type theorem for POVMs has also been proved. As an application it is shown that a map on any commutative unital $C^*$-algebra with countable spectrum (in particular ${\mathbb C}^n$) is $C^*$-extreme in the set of unital completely positive maps if and only if it is a unital $*$-homomorphism.

math.OA↗

arXiv2021

Pricing of debt and equity in a financial network with comonotonic endowments

In this paper we present formulas for the valuation of debt and equity of firms in a financial network under comonotonic endowments. We demonstrate that the comonotonic setting provides a lower bound and Jensen's inequality provides an upper bound to the price of debt under Eisenberg-Noe financial networks with bankruptcy costs. Such financial networks encode the interconnection of firms through debt claims. The proposed pricing formulas consider the realized, endogenous, recovery rate on debt claims. Special consideration is given to the CAPM setting in which firms invest in correlated portfolios so as to provide analytical stress testing formulas. We endogenously construct the comonotonic endowment setting from a equity maximizing standpoint with capital transfers. We conclude by, numerically, comparing the network valuation problem with two single firm baseline heuristics which can, respectively, approximate the price of debt and equity.

q-fin.MF↗

arXiv2021

Predicting Mood Disorder Symptoms with Remotely Collected Videos Using an Interpretable Multimodal Dynamic Attention Fusion Network

We developed a novel, interpretable multimodal classification method to identify symptoms of mood disorders viz. depression, anxiety and anhedonia using audio, video and text collected from a smartphone application. We used CNN-based unimodal encoders to learn dynamic embeddings for each modality and then combined these through a transformer encoder. We applied these methods to a novel dataset - collected by a smartphone application - on 3002 participants across up to three recording sessions. Our method demonstrated better multimodal classification performance compared to existing methods that employed static embeddings. Lastly, we used SHapley Additive exPlanations (SHAP) to prioritize important features in our model that could serve as potential digital markers.

cs.LG↗

arXiv2021

Price mediated contagion through capital ratio requirements with VWAP liquidation prices

We develop a framework for price-mediated contagion in financial systems where banks are forced to liquidate assets to satisfy a risk-weight based capital adequacy requirement. In constructing this modeling framework, we introduce a two-tier pricing structure: the volume weighted average price that is obtained by any bank liquidating assets and the terminal mark-to-market price used to account for all assets held at the end of the clearing process. We consider the case of multiple illiquid assets and develop conditions for the existence and uniqueness of clearing prices. We provide a closed-form representation for the sensitivity of these clearing prices to the system parameters, and use this result to quantify: (1) the cost of regulation, in stress scenarios, faced by the system as a whole and the individual banks, and (2) the value of providing bailouts to consider when such notions are financially advisable. Numerical case studies are provided to study the application of this model to data.

q-fin.MF↗

arXiv2019

A Generalizable Method for Automated Quality Control of Functional Neuroimaging Datasets

Over the last twenty five years, advances in the collection and analysis of fMRI data have enabled new insights into the brain basis of human health and disease. Individual behavioral variation can now be visualized at a neural level as patterns of connectivity among brain regions. Functional brain imaging is enhancing our understanding of clinical psychiatric disorders by revealing ties between regional and network abnormalities and psychiatric symptoms. Initial success in this arena has recently motivated collection of larger datasets which are needed to leverage fMRI to generate brain-based biomarkers to support development of precision medicines. Despite methodological advances and enhanced computational power, evaluating the quality of fMRI scans remains a critical step in the analytical framework. Before analysis can be performed, expert reviewers visually inspect raw scans and preprocessed derivatives to determine viability of the data. This Quality Control (QC) process is labor intensive, and the inability to automate at large scale has proven to be a limiting factor in clinical neuroscience fMRI research. We present a novel method for automating the QC of fMRI scans. We train machine learning classifiers using features derived from brain MR images to predict the "quality" of those images, based on the ground truth of an expert's opinion. We emphasize the importance of these classifiers' ability to generalize their predictions across data from different studies. To address this, we propose a novel approach entitled "FMRI preprocessing Log mining for Automated, Generalizable Quality Control" (FLAG-QC), in which features derived from mining runtime logs are used to train the classifier. We show that classifiers trained on FLAG-QC features perform much better (AUC=0.79) than previously proposed feature sets (AUC=0.56) when testing their ability to generalize across studies.

eess.IV↗

arXiv2019

Search for stabilizing effects of $\bm{Z=82}$ shell closure against fission

Presence of closed proton and/or neutron shells causes deviation from macroscopic properties of nuclei which are understood in terms of the liquid drop model. It is important to investigate experimentally the stabilizing effects of shell closure, if any, against fission. This work aims to investigate probable effects of proton shell ($Z = 82$) closure in the compound nucleus, in enhancing survival probability of the evaporation residues formed in heavy ion-induced fusion-fission reactions. Evaporation residue cross sections have been measured for the reactions $^{19}$F+$^{180}$Hf, $^{19}$F+$^{181}$Ta and $^{19}$F+$^{182}$W from $\simeq9\%$ below to $\simeq42\%$ above the Coulomb barrier, leading to formation of compound nuclei with same number of neutrons ($N = 118$) but different number of protons across $Z = 82$. Measured excitation functions have been compared with statistical model calculation, in which reduced dissipation coefficient is the only adjustable parameter. Evaporation residue cross section, normalized by capture cross section, is found to decrease gradually with increasing fissility of the compound nucleus. Measured evaporation residue cross sections require inclusion of nuclear viscosity in the model calculations. Reduced dissipation coefficient in the range of 1\textendash3 $\times$ $10^{21}$ s$^{-1}$ reproduces the data quite well. No abrupt enhancement of evaporation residue cross sections has been observed in the reaction forming compound nucleus with $Z = 82$. Thus, this work does not find enhanced stabilizing effects of $Z = 82$ shell closure against fission in the compound nucleus. One may attempt to measure cross sections of individual exit channels for further confirmation of our observation.

nucl-ex↗