arXiv subjects

Santosh Kumar

Publications and source records attributed to Santosh Kumar.

At least 55 records · Page 3Linked to original sources

arXiv2023

Multifractal dimensions for orthogonal-to-unitary crossover ensemble

Multifractal analysis is a powerful approach for characterizing ergodic or localized nature of eigenstates in complex quantum systems. In this context, the eigenvectors of random matrices belonging to invariant ensembles naturally serve as models for ergodic states. However, it has been found that the finite-size versions of multifractal dimensions for these eigenvectors converge to unity logarithmically slowly with increase in the system size $N$. In fact, this strong finite-size effect is capable of distinguishing the ergodicity behavior of orthogonal and unitary invariant classes. Motivated by this observation, in this work, we provide semi-analytical expressions for the ensemble-averaged multifractal dimensions associated with eigenvectors in the orthogonal-to-unitary crossover ensemble. Additionally, we explore shifted and scaled variants of multifractal dimensions, which, in contrast to the multifractal dimensions themselves, yield distinct values in the orthogonal and unitary limits as $N\to\infty$ and therefore may serve as a convenient measure for studying the crossover. We substantiate our results using Monte Carlo simulations of the underlying crossover random matrix model. We then apply our results to analyze the multifractal dimensions in a quantum kicked rotor, a Sinai billiard system, and a correlated spin chain model in a random field. The orthogonal-to-unitary crossover in these systems is realized by tuning relevant system parameters, and we find that in the crossover regime, the observed finite-dimension multifractal dimensions can be captured very well with our results.

quant-ph↗

arXiv2023

Analysis of system capacity and spectral efficiency of fixed-grid network

In this article, the performance of a fixed grid network is examined for various modulation formats to estimate the system's capacity and spectral efficiency. The optical In-phase Quadrature Modulator structure is used to build a fixed grid network modulation, and the homodyne detection approach is used for the receiver. Data multiplexing is accomplished using the Polarization Division Multiplexed technology. 100 Gbps, 150 Gbps, and 200 Gbps data rates are transmitted under these circumstances utilizing various modulation formats. Various pre-processing and signal recovery steps are explained by using modern digital signal processing systems. The achieved spectrum efficiencies for PM-QPSK, PM-8 QAM, and PM-16 QAM, respectively, were 2, 3, and 4 bits/s/Hz. Different modulation like PM-QPSK, PM-8-QAM, and PM-16-QAM each has system capacities of 8-9, 12-13.5, and 16-18 Tbps and it reaches transmission distances of 3000, 1300, and 700 kilometers with acceptable Bit Error Rate less than equal to 2*10-3 respectively. Peak optical power for received signal detection and full width at half maximum is noted for the different modulations under a fixed grind network.

cs.NI↗

arXiv2023

Spectral Crossovers and Universality in Quantum Spin-chains Coupled to Random Fields

We study the spectral properties of and spectral-crossovers between different random matrix ensembles (Poissonian, GOE, GUE) in correlated spin-chain systems, in the presence of random magnetic fields, and the scalar spin-chirality term, competing with the usual isotropic and time-reversal invariant Heisenberg term. We have investigated these crossovers in the context of the level-spacing distribution and the level-spacing ratio distribution. We use random matrix theory (RMT) analytical results to fit the observed Poissonian-to-GOE and GOE-to-GUE crossovers, and examine the relationship between the RMT crossover parameter λ and scaled physical parameters of the spin-chain systems in terms of a scaling exponent. We find that the crossover behavior exhibits universality, in the sense that it becomes independent of lattice size in the large Hamiltonian matrix dimension limit.

cond-mat.stat-mech↗

arXiv2023

Signatures of spectral crossovers in the short- and long-range spectral correlations of a disordered spin-chain with Kramers degeneracy

We investigate several distinct spectral crossovers amongst various integrable and quantum-chaotic limits of a 1D disordered quantum spin-1/2 model, by tuning the relative amplitudes of various Hamiltonian parameters to retain or break relevant unitary and antiunitary symmetries. Since we are specially interested in crossovers involving a Gaussian symplectic ensemble (GSE) limit, we carry out all our calculations with an odd number of spins that naturally results in eigenspectra with Kramers degeneracies. The various crossovers are investigated via detailed studies of both short-range (NNSD) and long-range (spectral rigidity and number variance) spectral correlations. The short-range studies show excellent agreement with RMT predictions. One of the highlights of this study is the systematic investigation of the consequences of retaining both eigenvalues corresponding to every Kramers doublet, in a crossover involving the GSE limit, and see how it evolves to a limit where the KD is naturally lifted. The NNSD plot in the GSE limit exhibits a Dirac delta peak at zero splitting and a renormalized GSE hump at finite splitting, whose general analytical form is derived. With an increasing symmetry breaking magnetic field the NNSD shows an interesting, dynamic two-peaked structure that finally converges to the standard GUE lineshape. We explain this trend in terms of a competition between the splittings amongst distinct Kramers doublets and the Zeeman-like splittings induced by a breaking of time-reversal symmetry. In the long-range spectral correlation studies, we shed light on the extent of agreement between our physical spin systems and RMT predictions. Our studies also show that the long-range correlations may serve to distinguish between the two Poissonian limits (nonlocalized and localized) in the reentrant crossovers, which the short-range correlations fail to distinguish.

cond-mat.stat-mech↗

arXiv2023

Flexible Beamforming in B5G for Improving Tethered UAV Coverage over Smart Environments

Unmanned Aerial Vehicles (UAVs) are being used for wireless communications in smart environments. However, the need for mobility, scalability of data transmission over wide areas, and the required coverage area make UAV beamforming essential for better coverage and user experience. To this end, we propose a flexible beamforming approach to improve tethered UAV coverage quality and maximize the number of users experiencing the minimum required rate in any target environment. Our solution demonstrates a significant achievement in flexible beamforming in smart environments, including urban, suburban, dense, and high-rise urban. Furthermore, the beamforming gain is mainly concentrated in the target to improve the coverage area based on various scenarios. Simulation results show that the proposed approach can achieve a significantly received flexible power beam that focuses the transmitted signal towards the receiver and improves received power by reducing signal power spread. In the case of no beamforming, signal power spreads out as distance increases, reducing the signal strength. Furthermore, our proposed solution is suitable for improving UAV coverage and reliability in smart and harsh environments.

eess.SP↗

arXiv2023

Inelastic neutron scattering investigation of the crystal field excitations of NdCo$_5$

We present an inelastic neutron scattering study of the crystal electric field levels in the intermetallic ferrimagnets RECo$_{5}$ (RE = Nd and Y). In NdCo$_{5}$, measurements at $5~$K reveal two levels at approximately 28.9 and 52.9 meV. Crystal field calculations including the exchange field $B_{\textrm{exc}}$ from the Co sites account for both of these, as well as the spectrum at temperatures above the spin-reorientation transition at $\sim 280$~K. In particular, it is found that both a large hexagonal crystal field parameter $A_{6}^6\langle r^6 \rangle$ and $B_{\textrm{exc}}$ are required to reproduce the data, with the latter having a much larger value than that deduced from previous computational and experimental studies. Our study sheds light on the delicate interplay of terms in the rare-earth Hamiltonian of RECo$_5$ systems, and is therefore expected to stimulate further experimental and computational work on the broader family of rare-earth permanent magnets.

cond-mat.str-el↗

arXiv2023

A Novel end-to-end Framework for Occluded Pixel Reconstruction with Spatio-temporal Features for Improved Person Re-identification

Person re-identification is vital for monitoring and tracking crowd movement to enhance public security. However, re-identification in the presence of occlusion substantially reduces the performance of existing systems and is a challenging area. In this work, we propose a plausible solution to this problem by developing effective occlusion detection and reconstruction framework for RGB images/videos consisting of Deep Neural Networks. Specifically, a CNN-based occlusion detection model classifies individual input frames, followed by a Conv-LSTM and Autoencoder to reconstruct the occluded pixels corresponding to the occluded frames for sequential (video) and non-sequential (image) data, respectively. The quality of the reconstructed RGB frames is further refined and fine-tuned using a Conditional Generative Adversarial Network (cGAN). Our method is evaluated on four well-known public data sets of the domain, and the qualitative reconstruction results are indeed appealing. Quantitative evaluation in terms of re-identification accuracy of the Siamese network showed an exceptional Rank-1 accuracy after occluded pixel reconstruction on various datasets. A comparative analysis with state-of-the-art approaches also demonstrates the robustness of our work for use in real-life surveillance systems.

cs.CV↗

arXiv2023

A Programmable Spatiotemporal Quantum Parametric Mode Sorter

We experimentally demonstrate a programmable parametric mode sorter of high-dimensional signals in a composite spatiotemporal Hilbert space through mode-selective quantum frequency up-conversion. As a concrete example and with quantum communication applications in mind, we consider the Laguerre-Gaussian and Hermite-Gaussian modes as the spatial and temporal state basis for the signals, respectively. By modulating the spatiotemporal profiles of the up-conversion pump, we demonstrate the faithful selection of single photons in those modes and their superposition modes. Our results show an improvement in the quantum mode-sorting performance by coupling the up-converted light into a single-mode fiber and/or operating the upconversion at the edge of phase matching. By optimizing pump temporal profiles only, we achieve more than 12 dB extinction for mutually unbiased basis (MUB) sets of the spatiotemporal modes. This fully programmable and efficient system could serve as a viable resource for quantum communications, quantum computation, and quantum metrology.

quant-ph↗

arXiv2022

Optical MIMO communication with unequal power allocation to channels

Multiple input multiple output (MIMO) approach in fiber optical communication has emerged as an effective proposition to address the ever increasing demand for information exchange. In the ergodic case, the multiple channels, associated with multiple modes or cores or both in the optical fiber, is modeled by the Jacobi ensemble of random matrices. A key quantity for assessing the performance of MIMO systems is the mutual information (MI). We focus here on the case of an arbitrary transmission covariance matrix and derive exact determinant based results for the moment generating function (MGF) of mutual information (MI), and thereby address the scenario of unequal power per excited mode. The MGF is used to obtain Gaussian- and Weibull-distribution based approximations for the probability density function (PDF), cumulative distribution function (CDF) or, equivalently, the outage probability, and also the survival function (SF) or reliability function. Moreover, a numerical Fourier inversion approach is implemented to obtain the PDF, CDF, and SF directly from the MGF. The MGF is further used to investigate the ergodic capacity, which is the first moment (mean) of the mutual information. The analytical results are found to be in excellent agreement with Monte Carlo simulations. Our study goes beyond the earlier investigations where covariance matrix proportional to identity matrix has been considered which corresponds to equal power allocation per excited mode.

cs.IT↗

arXiv2022

Random density matrices: Analytical results for mean root fidelity and mean square Bures distance

Bures distance holds a special place among various distance measures due to its several distinguished features and finds applications in diverse problems in quantum information theory. It is related to fidelity and, among other things, it serves as a bona fide measure for quantifying the separability of quantum states. In this work, we calculate exact analytical results for the mean root fidelity and mean square Bures distance between a fixed density matrix and a random density matrix, and also between two random density matrices. In the course of derivation, we also obtain spectral density for product of above pairs of density matrices. We corroborate our analytical results using Monte Carlo simulations. Moreover, we compare these results with the mean square Bures distance between reduced density matrices generated using coupled kicked tops and find very good agreement.

quant-ph↗

arXiv2022

Random density matrices: Analytical results for mean fidelity and variance of squared Bures distance

One of the key issues in quantum information theory related problems concerns with that of distinguishability of quantum states. In this context, Bures distance serves as one of the foremost choices among various distance measures. It also relates to fidelity, which is another quantity of immense importance in quantum information theory. In this work, we derive exact results for the average fidelity and variance of the squared Bures distance between a fixed density matrix and a random density matrix, and also between two independent random density matrices. These results supplement the recently obtained results for the mean root fidelity and mean of squared Bures distance [Phys. Rev. A 104, 022438 (2021)]. The availability of both mean and variance also enables us to provide a gamma-distribution-based approximation for the probability density of the squared Bures distance. The analytical results are corroborated using Monte Carlo simulations. Furthermore, we compare our analytical results with the mean and variance of the squared Bures distance between reduced density matrices generated using coupled kicked tops, and a correlated spin chain system in a random magnetic field. In both cases, we find good agreement.

quant-ph↗

arXiv2022

Near-Infrared 3D Imaging with Upconversion Detection

We demonstrate a photon-sensitive, three-dimensional camera by active near-infrared illumination and fast time-of-flight gating. It uses pico-second pump pulses to selectively up-convert the backscattered photons according to their spatiotemporal modes via sum-frequency generation in a \c{hi}2 nonlinear crystal, which are then detected by electron-multiplying CCD with photon sensitive detection. As such, it achieves sub-millimeter depth resolution, exceptional noise suppression, and high detection sensitivity. Our results show that it can accurately reconstruct the surface profiles of occluded targets placed behind highly scattering and lossy obscurants of 14 optical depth (round trip), using only milliwatt illumination power. This technique may find applications in biomedical imaging, environmental monitoring, and wide-field light detection and ranging

physics.optics↗

arXiv2022

Asymmetric rotations and dimerization driven by normal to modulated phase transition in 4-biphenylcarboxy coupled L-phenylalaninate

Amongst the derivatives of 4-biphenylcarboxylic acid and amino acid esters, the crystal structure of 4-biphenylcarboxy-(L)-phenylalaninate is unusual owing to its monoclinic symmetry within a pseudo-orthorhombic lattice. The distortion is described by disparate rotational property around the chiral centers ($φ_{\mathrm{chiral}}$ $\simeq$ -129 degrees and 58 degrees) of the two molecules in the asymmetric unit. Each of these molecules comprise of planar biphenyl moieties ($φ_{\mathrm{biphenyl}}$ = 0 degrees). Using temperature dependent single crystal X-ray diffraction experiments we show that the compound undergoes a phase transition below $T$ $\sim$ 124 K that is characterized by a commensurate modulation wave vector, $\mathbf{q}$ = $δ(101)$, $δ$ = $\frac{1}{2}$. The (3+1) dimensional modulated structure at $T$ = 100 K suggests that the phase transition drives the biphenyl moieties towards non coplanar conformations with significant variation of internal torsion ($φ^{\mathrm{max}}_{\mathrm{biphenyl}}$ $\leq$ $20$ degrees). These intramolecular rotations lead to dimerization of the molecular stacks that are described predominantly by intermolecular tilts and small variations in intermolecular distances. Atypical of modulated structures and superstructures of biphenyl and other polyphenyls, the rotations of individual molecules are asymmetric ($Δ$$φ_{\mathrm{biphenyl}}$ $\approx$ 5 degrees) while $φ_{\mathrm{biphenyl}}$ of one independent molecule is two to four times larger than the other. Crystal-chemical analysis and phase relations in superspace suggest multiple competing factors involving intramolecular steric factors, intermolecular H--C${\cdot}{\cdot}{\cdot}$C--H contacts and weak C--H${\cdot}{\cdot}{\cdot}$O hydrogen bonds that govern the distinctively unequal torsional property of the molecules.

cond-mat.mtrl-sci↗

arXiv2022

Observation of Distinct Phase Transitions in a Nonlinear Optical Ising Machine

Optical Ising machines promise to solve complex optimization problems with an optical hardware acceleration advantage. Here we study the ground state properties of a nonlinear optical Ising machine realized by spatial light modulator, Fourier optics, and second harmonic generation in a nonlinear crystal. By tuning the ratio of the light intensities at the fundamental and second harmonic frequencies, we experimentally observe two distinct ferromagnetic-to-paramagnetic phase transitions: a second-order phase transition where the magnetization changes to zero continuously and a first-order phase transition where the magnetization drops to zero abruptly as the effective temperature increases. Our experimental results are corroborated by a numerical simulation based on the Monte Carlo Metropolis-Hastings algorithm, and the physical mechanism for the distinct phase transitions can be understood with a mean-field theory. Our results showcase the great flexibility of the nonlinear optical Ising machine, which may find important potential applications in solving combinatorial optimization problems.

physics.optics↗

arXiv2021

Transformers for prompt-level EMA non-response prediction

Ecological Momentary Assessments (EMAs) are an important psychological data source for measuring current cognitive states, affect, behavior, and environmental factors from participants in mobile health (mHealth) studies and treatment programs. Non-response, in which participants fail to respond to EMA prompts, is an endemic problem. The ability to accurately predict non-response could be utilized to improve EMA delivery and develop compliance interventions. Prior work has explored classical machine learning models for predicting non-response. However, as increasingly large EMA datasets become available, there is the potential to leverage deep learning models that have been effective in other fields. Recently, transformer models have shown state-of-the-art performance in NLP and other domains. This work is the first to explore the use of transformers for EMA data analysis. We address three key questions in applying transformers to EMA data: 1. Input representation, 2. encoding temporal information, 3. utility of pre-training on improving downstream prediction task performance. The transformer model achieves a non-response prediction AUC of 0.77 and is significantly better than classical ML and LSTM-based deep learning models. We will make our a predictive model trained on a corpus of 40K EMA samples freely-available to the research community, in order to facilitate the development of future transformer-based EMA analysis works.

cs.CL↗

arXiv2021

Time-Spatial Mode Selective Quantum Frequency Converter

We experimentally demonstrate a mode-selective quantum frequency converter over a compound spatio-temporal Hilbert space. We show that our method can achieve high-extinction for high-dimensional quantum state tomography by selectively upconverting the signal modes with a modulated and delayed pump. By preparing the pump in optimized modes through adaptive feedback control, selective frequency conversion is demonstrated with up to 30 dB extinction. The simultaneous operations over high-dimensional degrees of freedom in both spatial and temporal domains can serve as a viable resource for photon-efficient quantum communications and computation.

quant-ph↗

arXiv2021

Efficient Frequency Doubling with Active Stabilization on Chip

Thin-film lithium niobate (TFLN) is superior for integrated nanophotonics due to its outstanding properties in nearly all aspects: strong second-order nonlinearity, fast and efficient electro-optic effects, wide transparency window, and little two photon absorption and free carrier scattering. Together, they permit highly integrated nanophotonic circuits capable of complex photonic processing by incorporating disparate elements on the same chip. Yet, there has to be a demonstration that synergizes those superior properties for system advantage. Here we demonstrate such a chip that capitalizes on TFLNs favorable ferroelectricity, high second-order nonlinearity, and strong electro-optic effects. It consists of a monolithic circuit integrating a Z-cut, quasi-phase matched microring with high quality factor and a phase modulator used in active feedback control. By Pound-Drever-Hall locking, it realizes stable frequency doubling at about 50% conversion with only milliwatt pump, marking the highest by far among all nanophotonic platforms with milliwatt pumping. Our demonstration addresses a long-outstanding challenge facing cavity-based optical processing, including frequency conversion, frequency comb generation, and all-optical switching, whose stable performance is hindered by photorefractive or thermal effects. Our results further establish TFLN as an excellent material capable of optical multitasking, as desirable to build multi-functional chip devices.

physics.optics↗

arXiv2021

Efficient Reservoir Computing using Field Programmable Gate Array and Electro-optic Modulation

We experimentally demonstrate a hybrid reservoir computing system consisting of an electro-optic modulator and field programmable gate array (FPGA). It implements delay lines and filters digitally for flexible dynamics and high connectivity, while supporting a large number of reservoir nodes. To evaluate the system's performance and versatility, three benchmark tests are performed. The first is the 10th order Nonlinear Auto-Regressive Moving Average test (NARMA-10), where the predictions of 1000 and 25,000 steps yield impressively low normalized root mean square errors (NRMSE's) of 0.142 and 0.148, respectively. Such accurate predictions over into the far future speak to its capability of large sample size processing, as enabled by the present hybrid design. The second is the Santa Fe laser data prediction, where a normalized mean square error (NMSE) of 6.73x10-3 is demonstrated. The third is the isolate spoken digit recognition, with a word error rate close to 0.34%. Accurate, versatile, flexibly reconfigurable, and capable of long-term prediction, this reservoir computing system could find a wealth of impactful applications in real-time information processing, weather forecasting, and financial analysis.

eess.SP↗