arXiv subjects

Ashutosh Singh

Publications and source records attributed to Ashutosh Singh.

At least 37 records · Page 2Linked to original sources

arXiv2025

Quantum simulations of nuclear resonances with variational methods

The many-body nature of nuclear physics problems poses significant computational challenges. These challenges become even more pronounced when studying the resonance states of nuclear systems, which are governed by the non-Hermitian Hamiltonian. Quantum computing, particularly for quantum many-body systems, offers a promising alternative, especially within the constraints of current noisy intermediate-scale quantum (NISQ) devices. This work aims to simulate nuclear resonances using quantum algorithms by developing a variational framework compatible with non-Hermitian Hamiltonians and implementing it fully on a quantum simulator. We employ the complex scaling technique to extract resonance positions classically and adapt it for quantum simulations using a two-step algorithm. First, we transform the non-Hermitian Hamiltonian into a Hermitian form by using the energy variance as a cost function within a variational framework. Second, we perform theta-trajectory calculations to determine optimal resonance positions in the complex energy plane. To address resource constraints on NISQ devices, we utilize Gray Code (GC) encoding to reduce qubit requirements. We first validate our approach using a schematic potential model that mimics a nuclear potential, successfully reproducing known resonance energies with high fidelity. We then extend the method to a more realistic alpha-alpha nuclear potential and compute the resonance energies with a basis size of 16, using only four qubits. This study demonstrates, for the first time, that the complete theta-trajectory method can be implemented on a quantum computer without relying on any classical input beyond the Hamiltonian. The results establish a scalable and efficient quantum framework for simulating resonance phenomena in nuclear systems. This work represents a significant step toward quantum simulations of open quantum systems.

quant-ph↗

arXiv2025

DNA codes from $(\text{\textbaro}, \mathfrak{d}, γ)$-constacyclic codes over $\mathbb{Z}_4+ω\mathbb{Z}_4$

This work introduces a novel approach to constructing DNA codes from linear codes over a non-chain extension of $\mathbb{Z}_4$. We study $(\text{\textbaro},\mathfrak{d}, γ)$-constacyclic codes over the ring $\mathfrak{R}=\mathbb{Z}_4+ω\mathbb{Z}_4, ω^2=ω,$ with an $\mathfrak{R}$-automorphism $\text{\textbaro}$ and a $\text{\textbaro}$-derivation $\mathfrak{d}$ over $\mathfrak{R}.$ Further, we determine the generators of the $(\text{\textbaro},\mathfrak{d}, γ)$-constacyclic codes over the ring $\mathfrak{R}$ of any arbitrary length and establish the reverse constraint for these codes. Besides the necessary and sufficient criterion to derive reverse-complement codes, we present a construction to obtain DNA codes from these reversible codes. Moreover, we use another construction on the $(\text{\textbaro},\mathfrak{d},γ)$-constacyclic codes to generate additional optimal and new classical codes. Finally, we provide several examples of $(\text{\textbaro},\mathfrak{d}, γ)$ constacyclic codes and construct DNA codes from established results. The parameters of these linear codes over $\mathbb{Z}_4$ are better and optimal according to the codes available at \cite{z4codes}.

cs.IT↗

arXiv2024

A generalized $Λ$CDM model with parameterized Hubble parameter in particle creation, viscous and $f(R)$ model framework

In this study, we construct a theoretical framework based on the generalized Hubble parameter form which may arise within the particle creation, viscous and $f(R)$ gravity theory. The Hubble parameter is scrutinized for its compatibility with the observational data relevant to the late-time universe. By using Bayesian statistical techniques based on $χ^{2}$ minimization method, we determine model parameters's best fit values for the cosmic chronometer and supernovae Pantheon datasets. For the best fit values, the cosmographic and physical parameters are analyzed to understand the cosmic dynamics in model. We also analyze the model section criterion in comparison to the $Λ$ cold dark matter model.

gr-qc↗

arXiv2024

Machine Learning Algorithms for Detecting Mental Stress in College Students

In today's world, stress is a big problem that affects people's health and happiness. More and more people are feeling stressed out, which can lead to lots of health issues like breathing problems, feeling overwhelmed, heart attack, diabetes, etc. This work endeavors to forecast stress and non-stress occurrences among college students by applying various machine learning algorithms: Decision Trees, Random Forest, Support Vector Machines, AdaBoost, Naive Bayes, Logistic Regression, and K-nearest Neighbors. The primary objective of this work is to leverage a research study to predict and mitigate stress and non-stress based on the collected questionnaire dataset. We conducted a workshop with the primary goal of studying the stress levels found among the students. This workshop was attended by Approximately 843 students aged between 18 to 21 years old. A questionnaire was given to the students validated under the guidance of the experts from the All India Institute of Medical Sciences (AIIMS) Raipur, Chhattisgarh, India, on which our dataset is based. The survey consists of 28 questions, aiming to comprehensively understand the multidimensional aspects of stress, including emotional well-being, physical health, academic performance, relationships, and leisure. This work finds that Support Vector Machines have a maximum accuracy for Stress, reaching 95\%. The study contributes to a deeper understanding of stress determinants. It aims to improve college student's overall quality of life and academic success, addressing the multifaceted nature of stress.

cs.LG↗

arXiv2024

Polarized Superradiance from CsPbBr3 Quantum Dot Superlattice with Controlled Inter-dot Electronic Coupling

Cooperative emission of photons from an ensemble of quantum dots (QDs) as superradiance can arise from the electronically coupled QDs with a coherent emitting excited state. This contrasts with superfluorescence (Dicke superradiance), where the cooperative photon emission occurs via a spontaneous buildup of coherence in an ensemble of incoherently excited QDs via their coupling to a common radiation mode. While superfluorescence has been observed in perovskite QD systems, reports of superradiance from the electronically coupled ensemble of perovskite QDs are rare. Here, we demonstrate the generation of polarized superradiance with a very narrow linewidth (<5 meV) and a large redshift (~200 meV) from the electronically coupled CsPbBr3 QD superlattice achieved through a combination of strong quantum confinement and ligand engineering. In addition to photon bunching at low excitation densities, the superradiance is polarized in contrast to the uncoupled exciton emission from the same superlattice. This finding suggests the potential for obtaining polarized cooperative photon emission via anisotropic electronic coupling in QD superlattices even when the intrinsic anisotropy of exciton transition in individual QDs is weak.

cond-mat.mes-hall↗

arXiv2024

Optical pumping controls anisotropic response in semi-Dirac system

Low-energy Fermions in semi-Dirac systems depict linear momentum dispersion along one direction while having the features of parabolic dispersion in the other direction. Equilibrium optical responses of such highly anisotropic dispersion are manifested in direction-dependent optical conductivity tensor. Going beyond the equilibrium framework, here we probe the effects of optical pumping-led non-equilibrium carrier distribution on this system's transmission and polarization rotation. Within the equation of motion approach for a two-band density matrix, we obtain a quasi-steady state solution for a continuous wave (CW) illumination, in which the population of the two bands is characterized by non-thermal occupancy factors with a strong dependence on the amplitude, polarization, and the frequency of the pump field. We demonstrate that tuning the pump field parameters significantly modifies the optical conductivity tensor for the probe field, which can have important practical consequences such as selective transmission and tunable hyperbolicity.

cond-mat.mes-hall↗

arXiv2024

KODA: A Data-Driven Recursive Model for Time Series Forecasting and Data Assimilation using Koopman Operators

Approaches based on Koopman operators have shown great promise in forecasting time series data generated by complex nonlinear dynamical systems (NLDS). Although such approaches are able to capture the latent state representation of a NLDS, they still face difficulty in long term forecasting when applied to real world data. Specifically many real-world NLDS exhibit time-varying behavior, leading to nonstationarity that is hard to capture with such models. Furthermore they lack a systematic data-driven approach to perform data assimilation, that is, exploiting noisy measurements on the fly in the forecasting task. To alleviate the above issues, we propose a Koopman operator-based approach (named KODA - Koopman Operator with Data Assimilation) that integrates forecasting and data assimilation in NLDS. In particular we use a Fourier domain filter to disentangle the data into a physical component whose dynamics can be accurately represented by a Koopman operator, and residual dynamics that represents the local or time varying behavior that are captured by a flexible and learnable recursive model. We carefully design an architecture and training criterion that ensures this decomposition lead to stable and long-term forecasts. Moreover, we introduce a course correction strategy to perform data assimilation with new measurements at inference time. The proposed approach is completely data-driven and can be learned end-to-end. Through extensive experimental comparisons we show that KODA outperforms existing state of the art methods on multiple time series benchmarks such as electricity, temperature, weather, lorenz 63 and duffing oscillator demonstrating its superior performance and efficacy along the three tasks a) forecasting, b) data assimilation and c) state prediction.

cs.LG↗

arXiv2024

Variance Residual Life Ageing Intensity Function

Quantitative measurement of ageing across systems and components is crucial for accurately assessing reliability and predicting failure probabilities. This measurement supports effective maintenance scheduling, performance optimisation, and cost management. Examining the ageing characteristics of a system that operates beyond a specified time $t > 0$ yields valuable insights. This paper introduces a novel metric for ageing, termed the Variance Residual Life Ageing Intensity (VRLAI) function, and explores its properties across various probability distributions. Additionally, we characterise the closure properties of the two ageing classes defined by the VRLAI function. We propose a new ordering, called the Variance Residual Life Ageing Intensity (VRLAI) ordering, and discuss its various properties. Furthermore, we examine the closure of the VRLAI order under coherent systems.

math.ST↗

arXiv2024

Dynamical systems of modified Gauss-Bonnet gravity: cosmological implications

In this paper, we derive the field equations of modified Gauss-Bonnet gravity termed as $f(R,G)$ gravity for the non-flat Friedmann-Robertson-Walker (FRW) spacetime. We utilize the dynamical system approach to study the cosmic dynamics of two different class of $f(R,G)$ models composed of radiation and matter (cold dark matter and baryonic matter). The linear perturbations around the fixed points are studied to explore the corresponding stability of points. The cosmological implications are studied in $f(R,G)=f_0R^nG^{1-n}$ and $f(R,G)=f_0R^α+f_1G^β$ models to identify the qualitative evolution of universe with the flat-FRW spacetime. The qualitative differences between the considered class of models are discussed in detail. The fixed points corresponding to the late-time accelerated and radiation phase of the universe will exist in the model but, the existence of fixed point corresponding to the matter dominated phase will depend on the functional form of $f(R,G)$. Furthermore, the autonomous systems are utilized to study the cosmographic parameters along with the statefinder diagnostic.

gr-qc↗

arXiv2024

Double skew cyclic codes over $\mathbb{F}_q+v\mathbb{F}_q$

In this study, in order to get better codes, we focus on double skew cyclic codes over the ring $\mathrm{R}= \mathbb{F}_q+v\mathbb{F}_q, ~v^2=v$ where $q$ is a prime power. We investigate the generator polynomials, minimal spanning sets, generator matrices, and the dual codes over the ring $\mathrm{R}$. As an implementation, the obtained results are illustrated with some good examples. Moreover, we introduce a construction for new generator matrices and thus achieve codes with improved parameters compared to those found in existing literature. Finally, we tabulate our obtained block codes over the ring $\mathrm{R}$.

cs.IT↗

arXiv2024

Coherent optical control of quantum Hall edge states

Current carrying chiral edge states in quantum Hall systems have fascinating properties that are usually studied by electron spectroscopy and interferometry. Here we demonstrate that electron occupation, current, and electron coherence in chiral edge states can be selectively probed and controlled by low-energy electromagnetic radiation in the microwave to infrared range without affecting electron states in the bulk or destroying quantum Hall effect conditions in the bulk of the sample. Both linear and nonlinear optical control is possible due to inevitable violation of adiabaticity and inversion symmetry breaking for electron states near the edge. This opens up new pathways for frequency- and polarization-selective spectroscopy and control of individual edge states.

cond-mat.mes-hall↗

arXiv2024

Learning Semilinear Neural Operators : A Unified Recursive Framework For Prediction And Data Assimilation

Recent advances in the theory of Neural Operators (NOs) have enabled fast and accurate computation of the solutions to complex systems described by partial differential equations (PDEs). Despite their great success, current NO-based solutions face important challenges when dealing with spatio-temporal PDEs over long time scales. Specifically, the current theory of NOs does not present a systematic framework to perform data assimilation and efficiently correct the evolution of PDE solutions over time based on sparsely sampled noisy measurements. In this paper, we propose a learning-based state-space approach to compute the solution operators to infinite-dimensional semilinear PDEs. Exploiting the structure of semilinear PDEs and the theory of nonlinear observers in function spaces, we develop a flexible recursive method that allows for both prediction and data assimilation by combining prediction and correction operations. The proposed framework is capable of producing fast and accurate predictions over long time horizons, dealing with irregularly sampled noisy measurements to correct the solution, and benefits from the decoupling between the spatial and temporal dynamics of this class of PDEs. We show through experiments on the Kuramoto-Sivashinsky, Navier-Stokes and Korteweg-de Vries equations that the proposed model is robust to noise and can leverage arbitrary amounts of measurements to correct its prediction over a long time horizon with little computational overhead.

cs.LG↗

arXiv2023

Construction of $(σ,δ)$-cyclic codes over a non-chain ring and their applications in DNA codes

For a prime $p$ and a positive integer $m$, let $\mathbb{F}_{p^m}$ be the finite field of characteristic $p$, and $\mathfrak{R}_l:=\mathbb{F}_{p^m}[v]/\langle v^l-v\rangle$ be a non-chain ring. In this paper, we study the $(σ,δ)$-cyclic codes over $\mathfrak{R}_l$. Further, we study the application of these codes in finding DNA codes. Towards this, we first define a Gray map to find classical codes over $\mathbb{F}_{p^m}$ using codes over the ring $\mathfrak{R}_l$. Later, we find the conditions for a code to be reversible and a DNA code using $(σ, δ)$-cyclic code. Finally, this algebraic method provides many classical and DNA codes of better parameters.

cs.IT↗

arXiv2023

Hyperbolic polaritons in topological nodal ring semimetals

In mirror-symmetric systems, there is a possibility of the realization of extended gapless electronic states characterized as nodal lines or rings. Strain induced modifications to these states lead to emergence of different classes of nodal rings with qualitatively different physical properties. Here we study optical response and the electromagnetic wave propagation in type I nodal ring semimetals, in which the low-energy quasiparticle dispersion is parabolic in momentum $k_x$ and $k_y$ and is linear in $k_z$. This leads to a highly anisotropic dielectric permittivity tensor in which the optical response is plasmonic in one spatial direction and dielectric in the other two directions. The resulting normal modes (polaritons) in the bulk material become hyperbolic over a broad frequency range, which is furthermore tunable by the doping level. The propagation, reflection, and polarization properties of the hyperbolic polaritons not only provide valuable information about the electronic structure of these fascinating materials in the most interesting region near the nodal rings but also pave the way to tunable hyperbolic materials with applications ranging from anomalous refraction and waveguiding to perfect absorption in ultrathin subwavelength films.

cond-mat.mes-hall↗

arXiv2023

Field-theoretic functional renormalization group formalism for non-Fermi liquids and its application to the antiferromagnetic quantum critical metal in two dimensions

To capture the universal low-energy physics of metals within effective field theories, one has to generalize the usual notion of scale invariance and renormalizable field theory due to the presence of intrinsic scales (Fermi momenta). In this paper, we develop a field-theoretic functional renormalization group formalism for full low-energy effective field theories of non-Fermi liquids that include all gapless modes around the Fermi surface. The formalism is applied to the non-Fermi liquid that arises at the antiferromagnetic quantum critical point in two space dimensions. In the space of coupling functions, an interacting fixed point arises at a point with momentum-independent couplings and vanishing nesting angle. In theories deformed with non-zero nesting angles, coupling functions acquire universal momentum profiles controlled by the bare nesting angles at low energies before flowing to superconducting states in the low-energy limit. The superconducting instability is unavoidable because lukewarm electrons that are coherent enough to be susceptible to pairing end up being subject to a renormalized attractive interaction with its minimum strength set by the nesting angle. Despite the inevitable superconducting instability, theories with small bare nesting angles and bare four-fermion couplings that are repulsive or weakly attractive must pass through the region with slow RG flow due to the proximity to the non-Fermi liquid fixed point. The bottleneck region controls the scaling behaviours of the normal state and the quasi-universal pathway from the non-Fermi liquid to superconductivity. In the limit that the nesting angle is small, the non-Fermi liquid scaling dictates the physics over a large window of energy scale above the superconducting transition temperature.

cond-mat.str-el↗

arXiv2022

Quantum Codes from additive constacyclic codes over a mixed alphabet and the MacWilliams identities

Let $\mathbb{Z}_p$ be the ring of integers modulo a prime number $p$ where $p-1$ is a quadratic residue modulo $p$. This paper presents the study of constacyclic codes over chain rings $\mathcal{R}=\frac{\mathbb{Z}_p[u]}{\langle u^2\rangle}$ and $\mathcal{S}=\frac{\mathbb{Z}_p[u]}{\langle u^3\rangle}$. We also study additive constacyclic codes over $\mathcal{R}\mathcal{S}$ and $\mathbb{Z}_p\mathcal{R}\mathcal{S}$ using the generator polynomials over the rings $\mathcal{R}$ and $\mathcal{S},$ respectively. Further, by defining Gray maps on $\mathcal{R}$, $\mathcal{S}$ and $\mathbb{Z}_p\mathcal{R}\mathcal{S},$ we obtain some results on the Gray images of additive codes. Then we give the weight enumeration and MacWilliams identities corresponding to the additive codes over $\mathbb{Z}_p\mathcal{R}\mathcal{S}$. Finally, as an application of the obtained codes, we give quantum codes using the CSS construction.

cs.IT↗

arXiv2022

Creation, Characterization, and Manipulation of Quantum Entanglement in a Photonic System

In this thesis, we report the theoretical and experimental investigations towards the creation, characterization, and manipulation of quantum entanglement in a photonic system. We examine two different aspects of quantum entanglement: In the first part, we discuss the experimental method for the preparation and characterization of SPDC-based polarization-entangled photon source. We provide a review study comparing different Entanglement Measures for non-maximally entangled two-qubit pure states and extend this analysis to higher-dimensional systems. In the second part, we study the entanglement dynamics of a two-qubit system in the presence of an Amplitude Damping Channel and present a scheme based on local unitary operations to protect entanglement from undergoing Entanglement Sudden Death. We extend the decoherence study to qubit-qutrit and qutrit-qutrit entangled systems and propose an entanglement protection scheme for the higher dimensional system.

quant-ph↗

arXiv2022

Inv-SENnet: Invariant Self Expression Network for clustering under biased data

Subspace clustering algorithms are used for understanding the cluster structure that explains the dataset well. These methods are extensively used for data-exploration tasks in various areas of Natural Sciences. However, most of these methods fail to handle unwanted biases in datasets. For datasets where a data sample represents multiple attributes, naively applying any clustering approach can result in undesired output. To this end, we propose a novel framework for jointly removing unwanted attributes (biases) while learning to cluster data points in individual subspaces. Assuming we have information about the bias, we regularize the clustering method by adversarially learning to minimize the mutual information between the data and the unwanted attributes. Our experimental result on synthetic and real-world datasets demonstrate the effectiveness of our approach.

cs.LG↗