arXiv subjects

Harpreet Singh

Publications and source records attributed to Harpreet Singh.

At least 37 records · Page 2Linked to original sources

arXiv2023

Characterization of single shallow silicon-vacancy centers in 4H-SiC

Shallow negatively charged silicon-vacancy centers have applications in magnetic quantum sensing and other quantum applications. Vacancy centers near the surface (within 100 nm) have different spin relaxation rates and optical spin polarization, affecting the optically detected magnetic resonance (ODMR) signal. This makes it essential to characterize these centers. Here we present the relevant spin properties of such centers. ODMR with a contrast of up to 6 %, which is better than the state of the art, allowed us to determine the zero field splitting, which is relevant for most sensing applications. We also present intensity-correlation data to verify that the signal originates from a single center and to extract transition rates between different electronic states.

quant-ph↗

arXiv2022

Identification of different silicon vacancy centers in 6H-SiC

Silicon vacancies in silicon carbide (SiC) have been proposed as interesting candidates for quantum technology applications such as quantum sensing and quantum repeaters. SiC exists in many polytypes with different plane stacking sequences, and in each polytype, the vacancies can occupy a variety of different lattice sites. In this work, we characterize and identify the three most important charged silicon vacancies in the 6H-SiC polytype. We record the photoluminescence and continuous-wave optically detected magnetic resonance spectra at different radio-frequency power levels and different temperatures. We individually select the zero-phonon lines of the different silicon vacancies at low temperatures and record the corresponding optically detected magnetic resonance (ODMR) spectra. ODMR allows us to correlate optical and magnetic resonance spectra and thereby resolve a controversy from earlier work.

cond-mat.mes-hall↗

arXiv2022

Medical Question Understanding and Answering with Knowledge Grounding and Semantic Self-Supervision

Current medical question answering systems have difficulty processing long, detailed and informally worded questions submitted by patients, called Consumer Health Questions (CHQs). To address this issue, we introduce a medical question understanding and answering system with knowledge grounding and semantic self-supervision. Our system is a pipeline that first summarizes a long, medical, user-written question, using a supervised summarization loss. Then, our system performs a two-step retrieval to return answers. The system first matches the summarized user question with an FAQ from a trusted medical knowledge base, and then retrieves a fixed number of relevant sentences from the corresponding answer document. In the absence of labels for question matching or answer relevance, we design 3 novel, self-supervised and semantically-guided losses. We evaluate our model against two strong retrieval-based question answering baselines. Evaluators ask their own questions and rate the answers retrieved by our baselines and own system according to their relevance. They find that our system retrieves more relevant answers, while achieving speeds 20 times faster. Our self-supervised losses also help the summarizer achieve higher scores in ROUGE, as well as in human evaluation metrics. We release our code to encourage further research.

cs.CL↗

arXiv2022

RerrFact: Reduced Evidence Retrieval Representations for Scientific Claim Verification

Exponential growth in digital information outlets and the race to publish has made scientific misinformation more prevalent than ever. However, the task to fact-verify a given scientific claim is not straightforward even for researchers. Scientific claim verification requires in-depth knowledge and great labor from domain experts to substantiate supporting and refuting evidence from credible scientific sources. The SciFact dataset and corresponding task provide a benchmarking leaderboard to the community to develop automatic scientific claim verification systems via extracting and assimilating relevant evidence rationales from source abstracts. In this work, we propose a modular approach that sequentially carries out binary classification for every prediction subtask as in the SciFact leaderboard. Our simple classifier-based approach uses reduced abstract representations to retrieve relevant abstracts. These are further used to train the relevant rationale-selection model. Finally, we carry out two-step stance predictions that first differentiate non-relevant rationales and then identify supporting or refuting rationales for a given claim. Experimentally, our system RerrFact with no fine-tuning, simple design, and a fraction of model parameters fairs competitively on the leaderboard against large-scale, modular, and joint modeling approaches. We make our codebase available at https://github.com/ashishrana160796/RerrFact.

cs.CL↗

arXiv2022

Prediction of COVID-19 using chest X-ray images

COVID-19, also known as Novel Coronavirus Disease, is a highly contagious disease that first surfaced in China in late 2019. SARS-CoV-2 is a coronavirus that belongs to the vast family of coronaviruses that causes this disease. The sickness originally appeared in Wuhan, China in December 2019 and quickly spread to over 213 nations, becoming a global pandemic. Fever, dry cough, and tiredness are the most typical COVID-19 symptoms. Aches, pains, and difficulty breathing are some of the other symptoms that patients may face. The majority of these symptoms are indicators of respiratory infections and lung abnormalities, which radiologists can identify. Chest x-rays of COVID-19 patients seem similar, with patchy and hazy lungs rather than clear and healthy lungs. On x-rays, however, pneumonia and other chronic lung disorders can resemble COVID-19. Trained radiologists must be able to distinguish between COVID-19 and an illness that is less contagious. Our AI algorithm seeks to give doctors a quantitative estimate of the risk of deterioration. So that patients at high risk of deterioration can be triaged and treated efficiently. The method could be particularly useful in pandemic hotspots when screening upon admission is important for allocating limited resources like hospital beds.

eess.IV↗

arXiv2021

Multi-photon multi-quantum transitions in the spin-3/2 silicon-vacancy centers of SiC

Silicon vacancy centers in silicon carbide are promising candidates for storing and manipulating quantum information. Implementation of fast quantum gates is an essential requirement for quantum information processing. In a low magnetic field, the resonance frequencies of silicon vacancy spins are in the range of a few MHz, the same order of magnitude as the Rabi frequencies of typical control fields. As a consequence, the rotating wave approximation becomes invalid and nonlinear processes like the absorption and emission of multiple photons become relevant. This work focuses on multi-photon transitions of negatively charged silicon vacancies driven by a strong RF field. We present continuous-wave optically detected magnetic resonance (ODMR) spectra measured at different RF powers to identify the 1-, 2-, and 3-RF photon transitions of different types of the silicon vacancy in the 6$H$-SIC polytype. Time-resolved experiments of Rabi oscillations and free induction decays of these multiple RF photon transitions were observed for the first time. Apart from zero-field data, we also present spectra in magnetic fields with different strength and orientation with respect to the system's symmetry axis.

quant-ph↗

arXiv2021

Optical spin initialization of spin-3/2 silicon vacancy centers in 6H-SiC at room temperature

Silicon vacancies in silicon carbide have been proposed as an alternative to nitrogen vacancy centers in diamonds for spintronics and quantum technologies. An important precondition for these applications is the initialization of the qubits into a specific quantum state. In this work, we study the optical alignment of the spin 3/2 negatively charged silicon vacancy in 6H-SiC. Using a time-resolved optically detected magnetic resonance technique, we coherently control the silicon vacancy spin ensemble and measure Rabi frequencies and spin-lattice relaxation time of all three transitions. Then to study the optical initialization process of the silicon vacancy spin ensemble, the vacancy spin ensemble is prepared in different ground states and optically excited. We describe a simple rate equation model that can explain the observed behaviour and determine the relevant rate constants.

quant-ph↗

arXiv2020

Using a Lindbladian approach to model decoherence in two coupled nuclear spins via correlated phase-damping and amplitude damping noise channels

In this work, we studied the relaxation dynamics of coherences of different order present in a system of two coupled nuclear spins. We used a previously designed model for intrinsic noise present in such systems which considers the Lindblad master equation for Markovian relaxation. We experimentally created zero-, single- and double- quantum coherences in several two-spin systems and performed a complete state tomography and computed state fidelity. We experimentally measured the decay of zero- and double- quantum coherences in these systems. The experimental data fitted well to a model that considers the main noise channels to be a correlated phase damping channel acting simultaneously on both spins in conjunction with a generalized amplitude damping channel acting independently on both spins. The differential relaxation of multiple-quantum coherences can be ascribed to the action of a correlated phase damping channel acting simultaneously on both the spins.

quant-ph↗

arXiv2020

Systematically designing better instance counting models on cell images with Neural Arithmetic Logic Units

The big problem for neural network models which are trained to count instances is that whenever test range goes high training range generalization error increases i.e. they are not good generalizers outside training range. Consider the case of automating cell counting process where more dense images with higher cell counts are commonly encountered as compared to images used in training data. By making better predictions for higher ranges of cell count we are aiming to create better generalization systems for cell counting. With architecture proposal of neural arithmetic logic units (NALU) for arithmetic operations, task of counting has become feasible for higher numeric ranges which were not included in training data with better accuracy. As a part of our study we used these units and different other activation functions for learning cell counting task with two different architectures namely Fully Convolutional Regression Network and U-Net. These numerically biased units are added in the form of residual concatenated layers to original architectures and a comparative experimental study is done with these newly proposed changes. This comparative study is described in terms of optimizing regression loss problem from these models trained with extensive data augmentation techniques. We were able to achieve better results in our experiments of cell counting tasks with introduction of these numerically biased units to already existing architectures in the form of residual layer concatenation connections. Our results confirm that above stated numerically biased units does help models to learn numeric quantities for better generalization results.

cs.LG↗

arXiv2020

Anomalous Motion Detection on Highway Using Deep Learning

Research in visual anomaly detection draws much interest due to its applications in surveillance. Common datasets for evaluation are constructed using a stationary camera overlooking a region of interest. Previous research has shown promising results in detecting spatial as well as temporal anomalies in these settings. The advent of self-driving cars provides an opportunity to apply visual anomaly detection in a more dynamic application yet no dataset exists in this type of environment. This paper presents a new anomaly detection dataset - the Highway Traffic Anomaly (HTA) dataset - for the problem of detecting anomalous traffic patterns from dash cam videos of vehicles on highways. We evaluate state-of-the-art deep learning anomaly detection models and propose novel variations to these methods. Our results show that state-of-the-art models built for settings with a stationary camera do not translate well to a more dynamic environment. The proposed variations to these SoTA methods show promising results on the new HTA dataset.

cs.CV↗

arXiv2020

Experimental characterization of spin-3/2 silicon vacancy centers in 6H-SiC

Silicon carbide (SiC) hosts many interesting defects that can potentially serve as qubits for a range of advanced quantum technologies. Some of them have very interesting properties, making them potentially useful, e.g. as interfaces between stationary and flying qubits. Here we present a detailed overview of the relevant properties of the spins in silicon vacancies of the 6H-SiC polytype. This includes the temperature-dependent photoluminescence, optically detected magnetic resonance, and the relaxation times of the longitudinal and transverse components of the spins, during free precession as well as under the influence of different refocusing schemes.

quant-ph↗

arXiv2018

Octet Baryon Masses and Magnetic Moments in Hot and Dense Isospin Asymmetric Nuclear Matter

Medium modification of the magnetic moments of octet baryons in the isospin asymmetric nuclear medium at finite temperature has been calculated using medium modified quark and baryon masses derived in chiral SU(3) quark mean field model. The magnetic moments of baryons are found to vary significantly as a function of density of nuclear medium as well as with the increase in isospin asymmetry of the medium. The rise of temperature is found to decrease the effect of isospin asymmetry on masses and magnetic moments of baryons. The results of present investigation may be helpful to understand the experimentally observed values of octet baryon magnetic moments at different experimental facilities.

nucl-th↗

arXiv2018

In-medium masses and magnetic moments of decuplet baryons

The magnetic moments of baryon decuplet are studied in vacuum as well as in the symmetric nuclear matter at finite temperature using a chiral SU(3) quark mean field model approach. The contributions coming from the valence quarks, quark sea and the orbital angular momentum of the quark sea have been considered to calculate magnetic moment of decuplet baryons. The decuplet baryon masses decrease, whereas, the magnetic moments increase significantly with the rise of baryonic density of the symmetric nuclear medium. This is because of the reason that constituent quark magnetic moment and the quark spin polarizations show considerable variation in the nuclear medium especially in the low temperature and baryonic density regime. The increase is however quantitatively less as compared to the case of octet baryon members.

hep-ph↗

arXiv2018

Experimental Classification of Entanglement in Arbitrary Three-Qubit States on an NMR Quantum Information Processor

We undertake experimental detection of the entanglement present in arbitrary three-qubit pure quantum states on an NMR quantum information processor. Measurements of only four observables suffice to experimentally differentiate between the six classes of states which are inequivalent under stochastic local operation and classical communication (SLOCC). The experimental realization is achieved by mapping the desired observables onto Pauli $z$-operators of a single qubit, which is directly amenable to measurement. The detection scheme is applied to known entangled states as well as to states randomly generated using a generic scheme that can construct all possible three-qubit states. The results are substantiated via direct full quantum state tomography as well as via negativity calculations and the comparison suggests that the protocol is indeed successful in detecting tripartite entanglement without requiring any {\it a priori} information about the states.

quant-ph↗

arXiv2018

Generation, estimation, and protection of novel quantum states of spin systems

This thesis deals with the generation, estimation and preservation of novel quantum states of two and three qubits, on an NMR quantum information processor. Using the maximum likelihood ansatz, we have developed a method for state estimation such that the reconstructed density matrix does not have negative eigenvalues and the errors are within the space of valid density operators. Due to interactions with the environment, unwanted changes occur in the system, leading to decoherence. Controlling decoherence is one of the biggest challenges to be overcome to build quantum computers. We have used several experimental strategies to decouple the quantum system from its environment. These strategies are based on how much we know about system-environment interaction and what states we want to preserve. We first consider a case where we are aware of the system state but have no knowledge about its interaction with the environment. We demonstrate the efficacy of the super-Zeno scheme to tackle decoherence in this case. Then we consider a situation where only the subspace is known to which the system state belongs. To address such a situation we used a nested Uhrig dynamical decoupling scheme. Next, we consider situations where we have knowledge of the state of the system as well as its interaction with the environment. In such situations, since the noise model is known, decoupling strategies can be explicitly designed to cancel this noise. Using these decoupling strategies, we can experimentally extend the lifetime of time-invariant discord of two-qubit Bell-diagonal states and are also able to preserve the entanglement of three NMR qubits, to a remarkable extent.

quant-ph↗

arXiv2018

Evolution of tripartite entangled states in a decohering environment and their experimental protection using dynamical decoupling

We embarked upon the task of experimental protection of different classes of tripartite entangled states, namely the maximally entangled GHZ and W states and the ${\rm W \bar{W}}$ state, using dynamical decoupling. The states were created on a three-qubit NMR quantum information processor and allowed to evolve in the naturally noisy NMR environment. Tripartite entanglement was monitored at each time instant during state evolution, using negativity as an entanglement measure. It was found that the W state is most robust while the GHZ-type states are most fragile against the natural decoherence present in the NMR system. The ${\rm W \bar{W}}$ state which is in the GHZ-class, yet stores entanglement in a manner akin to the W state, surprisingly turned out to be more robust than the GHZ state. The experimental data were best modeled by considering the main noise channel to be an uncorrelated phase damping channel acting independently on each qubit, alongwith a generalized amplitude damping channel. Using dynamical decoupling, we were able to achieve a significant protection of entanglement for GHZ states. There was a marginal improvement in the state fidelity for the W state (which is already robust against natural system decoherence), while the ${\rm W \bar{W}}$ state showed a significant improvement in fidelity and protection against decoherence.

quant-ph↗

arXiv2017

Efficient experimental design of high-fidelity three-qubit quantum gates via genetic programming

We have designed efficient quantum circuits for the three-qubit Toffoli (controlled-controlled NOT) and the Fredkin (controlled-SWAP) gate, optimized via genetic programming methods. The gates thus obtained were experimentally implemented on a three-qubit NMR quantum information processor, with a high fidelity. Toffoli and Fredkin gates in conjunction with the single-qubit Hadamard gates form a universal gate set for quantum computing, and are an essential component of several quantum algorithms. Genetic algorithms are stochastic search algorithms based on the logic of natural selection and biological genetics and have been widely used for quantum information processing applications. The numerically optimized rf pulse profiles of the three-qubit quantum gates achieve $> 99\%$ fidelity. The optimization was performed under the constraint that the experimentally implemented pulses are of short duration and can be implemented with high fidelity. Therefore the gate implementations do not suffer from the drawbacks of rf offset errors or debilitating effects of decoherence during gate action. We demonstrate the advantage of our pulse sequences by comparing our results with existing experimental schemes.

quant-ph↗

arXiv2017

Magnetic Moments of Octet Baryons in Hot and Dense Nuclear Matter

We have calculated the in-medium magnetic moments of octet baryons in the presence of hot and dense symmetric nuclear matter. Effective magnetic moments of baryons have been derived from medium modified quark masses within chiral SU(3) quark mean field model. Further, for better insight of medium modification of baryonic magnetic moments, we have considered the explicit contributions from the valence quarks, sea quarks as well as sea orbital angular momentum of sea quarks. These effects have been successful in giving the description of baryonic magnetic moments in vacuum. The magnetic moments of baryons are found to vary significantly as a function of density of nuclear medium.

hep-ph↗