arXiv subjects

Sadegh Raeisi

Publications and source records attributed to Sadegh Raeisi.

At least 19 recordsLinked to original sources

arXiv2025

Evaluation of Machine Learning Reconstruction Techniques for Accelerated Brain MRI Scans

This retrospective-prospective study evaluated whether a deep learning-based MRI reconstruction algorithm can preserve diagnostic quality in brain MRI scans accelerated up to fourfold, using both public and prospective clinical data. The study included 18 healthy volunteers (scans acquired at 3T, January 2024-March 2025), as well as selected fastMRI public datasets with diverse pathologies. Phase-encoding-undersampled 2D/3D T1, T2, and FLAIR sequences were reconstructed with DeepFoqus-Accelerate and compared with standard-of-care (SOC). Three board-certified neuroradiologists and two MRI technologists independently reviewed 36 paired SOC/AI reconstructions from both datasets using a 5-point Likert scale, while quantitative similarity was assessed for 408 scans and 1224 datasets using Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Haar wavelet-based Perceptual Similarity Index (HaarPSI). No AI-reconstructed scan scored below 3 (minimally acceptable), and 95% scored $\geq 4$. Mean SSIM was 0.95 $\pm$ 0.03 (90% cases >0.90), PSNR >41.0 dB, and HaarPSI >0.94. Inter-rater agreement was slight to moderate. Rare artifacts did not affect diagnostic interpretation. These findings demonstrate that DeepFoqus-Accelerate enables robust fourfold brain MRI acceleration with 75% reduced scan time, while preserving diagnostic image quality and supporting improved workflow efficiency.

eess.IV↗

arXiv2025

Tweets vs Pathogen Spread: A Case Study of COVID-19 in American States

The concept of the mutual influence that awareness and disease may exert on each other has recently presented significant challenges. The actions individuals take to prevent contracting a disease and their level of awareness can profoundly affect the dynamics of its spread. Simultaneously, disease outbreaks impact how people become aware. In response, we initially propose a null model that couples two Susceptible-Infectious-Recovered (SIR) dynamics and analyze it using a mean-field approach. Subsequently, we explore the parameter space to quantify the effects of this mutual influence on various observables. Finally, based on this null model, we conduct an empirical analysis of Twitter data related to COVID-19 and confirmed cases within American states. Our findings indicate that in specific regions of the parameter space, it is possible to suppress the epidemic by increasing awareness, and we investigate phase transitions. Furthermore, our model demonstrates the ability to alter the dominant population group by adjusting parameters throughout the course of the outbreak. Additionally, using the model, we assign a set of parameters to each state, revealing that these parameters change at different pandemic peaks. Notably, a robust correlation emerges between the ranking of states' Twitter activity, as gathered from empirical data, and the immunity parameters assigned to each state using our model. This observation underscores the pivotal role of sustained awareness transitioning from the initial to the subsequent peaks in the disease progression.

cs.SI↗

arXiv2023

Witnessing entanglement of remote particles with incomplete teleportation

Having common reference frames or aligned coordinate systems, is one of the presumptions in witnessing entanglement in a two-party state possessed by two remote parties. This assumption may fail for many reasons. With an unlimited supply of singlet states, the two parties can first align their coordinate systems and then measure any entanglement witness. In this article, we propose an alternative method which uses the same resource for incomplete teleportation of states between the two parties, enabeling them to witness the entanglement of any shared state by local measurements without the need of prior alignment of the coordinate systems. The method works for any kind of witness and in any dimension. Beyond the context of Entanglement Witnesses, our method works also for remote measurements of observables of particles (entangled or not) in laboratories which may have deficiency in their resources.

quant-ph↗

arXiv2023

Hierarchical Classification of Variable Stars Using Deep Convolutional Neural Networks

The importance of using fast and automatic methods to classify variable stars for large amounts of data is undeniable. There have been many attempts to classify variable stars by traditional algorithms like Random Forest. In recent years, neural networks as classifiers have come to notice because of their lower computational cost compared to traditional algorithms. This paper uses the Hierarchical Classification technique, which contains two main steps of predicting class and then subclass of stars. All the models in both steps have same network structure and we test both Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). Our pre-processing method uses light curves and period of stars as input data. We consider most of the classes and subclasses of variable stars in OGLE-IV database and show that using Hierarchical Classification technique and designing appropriate preprocessing can increase accuracy of predicting smaller classes, ACep and T2Cep. We obtain an accuracy of 98% for class classification and 93% for subclasses classification.

astro-ph.SR↗

arXiv2022

Automated Fake News Detection using cross-checking with reliable sources

Over the past decade, fake news and misinformation have turned into a major problem that has impacted different aspects of our lives, including politics and public health. Inspired by natural human behavior, we present an approach that automates the detection of fake news. Natural human behavior is to cross-check new information with reliable sources. We use Natural Language Processing (NLP) and build a machine learning (ML) model that automates the process of cross-checking new information with a set of predefined reliable sources. We implement this for Twitter and build a model that flags fake tweets. Specifically, for a given tweet, we use its text to find relevant news from reliable news agencies. We then train a Random Forest model that checks if the textual content of the tweet is aligned with the trusted news. If it is not, the tweet is classified as fake. This approach can be generally applied to any kind of information and is not limited to a specific news story or a category of information. Our implementation of this approach gives a $70\%$ accuracy which outperforms other generic fake-news classification models. These results pave the way towards a more sensible and natural approach to fake news detection.

cs.CL↗

arXiv2021

Cosmic velocity, density and halo mass function: Insights from deep learning

We discuss an implementation of a deep learning framework to gain insight into dark matter (DM) structure formation. We investigate the contribution of velocity and density field information to the construction of the halo mass function (HMF) in cosmological N-body simulations. We train a Convolutional Neural Network (CNN) on the initial snapshot of a DM-only simulation to predict the halo mass that individual particles fall into at $z = 0$, in the halo mass range of $10.5 < \log (M/M_{\odot}) < 14$. We show that for the standard $\Lambda$CDM cosmology with amplitude of initial perturbations $A_s = 2 \times 10^{-9}$, the initial velocity and density fields have equivalent information, as expected in the linear regime, and manifest the power of our CNN to diagnose the redundant information. To investigate the non-linear effects, we increase the initial power spectrum. In the linear regime, this is equivalent to decreasing the initial redshift. The CNN model trained on the simulation snapshots with large $A_s$ shows a considerable improvement in the HMF prediction when adding the velocity field information. Using our CNN map without further physical assumptions, we precisely evaluate when these non-linear effects become vital. Eventually, for the simulation with $A_s = 8 \times10^{-8}$, the model trained with only density information shows at least an $80\%$ increase in the mean squared error relative to the model with both velocity and density information. Our work shows the interpretability and ability of CNNs to read higher-order information from simple images, making them an excellent tool for cosmological studies.

astro-ph.CO↗

arXiv2021

No-go Theorem of Purification

The Shannon's bound for compression is one of the key restrictions for the compression of quantum information. Here we show that the unitarity of the compression operation imposes new bounds on the compression that are more limiting than Shannon's compression bound. This translates to a no-go theorem for the purification of quantum states. For a specific case of a two-qubit system, our results indicate that it is not possible to distill purity beyond the maximum of the individual purities. We show that this restriction results in the cooling limit of the heat-bath algorithmic cooling techniques. We formalize the limitations imposed by the unitarity of the compression operation in two theorems and use the theorems to show that the limitations of unitarity lead to the cooling limit of heat-bath algorithmic cooling. To this end, we introduce a new optimal cooling technique and show that without the limitations of the unitary operations, the new cooling technique would have exceeded the limit of Heat-bath algorithmic cooling. This work opens up new avenues to understanding the limits of dynamic cooling.

quant-ph↗

arXiv2021

Quantum noise can enhance algorithmic cooling

Heat-Bath Algorithmic Cooling techniques (HBAC) are techniques that are used to purify a target element in a quantum system. These methods compress and transfer entropy away from the target element into auxiliary elements of the system. The performance of Algorithmic Cooling has been investigated under ideal noiseless conditions. However, realistic implementations are imperfect and for practical purposes, noise should be taken into account. Here we analyze Heat-Bath Algorithmic Cooling techniques under realistic noise models. Surprisingly, we find that noise can in some cases enhance the performance and improve the cooling limit of Heat-Bath Algorithmic Cooling techniques. We numerically simulate the noisy algorithmic cooling for the two optimal strategies, the Partner Pairing, and the Two-sort algorithms. We find that for both of them, in the presence of the generalized amplitude damping noise, the process converges and the asymptotic purity can be higher than the noiseless process. This opens up new avenues for increasing the purity beyond the heat-bath algorithmic cooling.

quant-ph↗

arXiv2020

Coherence Based Characterization of Macroscopic Quantumness

One of the most elusive problems in quantum mechanics is the transition between classical and quantum physics. This problem can be traced back to the Schrödinger's cat. A key element that lies at the center of this problem is the lack of a clear understanding and characterization of macroscopic quantum states. Our understanding of Macroscopic Quantumness relies on states such as the Greenberger-Horne-Zeilinger(GHZ) or the NOON state. Here we take a first principle approach to this problem. We start from coherence as the key quantity that captures the notion of quantumness and demand the quantumness to be collective and macroscopic. To this end, we introduce macroscopic coherence which is the coherence between macroscopically distinct quantum states. We construct a measure that quantifies how global and collective the coherence of the state is. Our work also provides a first-principle way to derive well-established states like the GHZ and the NOON state as the states that maximize our measure. This new approach paves the way towards a better understanding of the Quantum-to-Classical transition.

quant-ph↗

arXiv2020

Finding semi-optimal measurements for entanglement detection using Autoencoder Neural Networks

Entanglement is one of the key resources of quantum information science which makes identification of entangled states essential to a wide range of quantum technologies and phenomena. This problem is however both computationally and experimentally challenging. Here we use autoencoder neural networks to find semi-optimal set of incomplete measurements that are most informative for the detection of entangled states. We show that it is possible to find high-performance entanglement detectors with as few as three measurements. Also, with the complete information of the state, we develop a neural network that can identify all two-qubits entangled states almost perfectly. This result paves the way for automatic development of efficient entanglement witnesses and entanglement detection using machine learning techniques.

quant-ph↗

arXiv2020

Experimental entropic test of state-independent contextuality via single photons

Recently, an inequality satisfied by non-contextual hidden-variable models and violated by quantum mechanics for all states of a four-level system has been derived based on information-theoretic distance approach to non-classical correlations. In this work, we experimentally demonstrate violation of this inequality with single photons. Our experiment offers a method to study a distinction between quantum and classical correlations from an information-theoretic perspective.

quant-ph↗

arXiv2019

Quench Dynamics in 1D Optomechanical Arrays

Non-equilibrium dynamics induced by rapid changes of external parameters is relevant for a wide range of scenarios across many domains of physics. For waves in spatially periodic systems, quenches will alter the bandstructure and generate new excitations. In the case of topological bandstructures, defect modes at boundaries can be generated or destroyed when quenching through a topological phase transition. Here, we demonstrate that optomechanical arrays are a promising platform for studying such dynamics, as their bandstructure can be tuned temporally by a control laser. We study the creation of nonequilibrium optical and mechanical excitations in 1D arrays, including a bosonic version of the Su-Schrieffer-Heeger model. These ideas can be transferred to other systems such as driven nonlinear cavity arrays.

quant-ph↗

arXiv2019

Novel Technique for Robust Optimal Algorithmic Cooling

Heat-bath algorithmic cooling (HBAC) provides algorithmic ways to improve the purity of quantum states. These techniques are complex iterative processes that change from each iteration to the next and this poses a significant challenge to implementing these algorithms. Here, we introduce a new technique that on a fundamental level, shows that it is possible to do algorithmic cooling and even reach the cooling limit without any knowledge of the state and using only a single fixed operation, and on a practical level, presents a more feasible and robust alternative for implementing HBAC. We also show that our new technique converges to the asymptotic state of HBAC and that the cooling algorithm can be efficiently implemented; however, the saturation could require exponentially many iterations and remains impractical. This brings HBAC to the realm of feasibility and makes it a viable option for realistic application in quantum technologies.

quant-ph↗

arXiv2019

Entanglement-assisted communication in the absence of shared reference frame

Alice wants to convey the value of a parameter to Bob with whom she does not share a reference frame. What physical object can she use for this task? Shall she encode this value into the angle between two physical vectors such as the angle between two spins? Can she benefit from using entanglement? We approach these questions here and show that an entangled state of two qubits has three parameters that are invariant under changes of the reference frame. We also calculate the average information gain, when each one of these parameters is used for communication. We compare our result with the special case of separable states and find that entanglement enhances the information gain.

quant-ph↗

arXiv2018

Experimental Saturation of the Heat-Bath Algorithmic Cooling bound

Heat-Bath Algorithmic cooling (HBAC) techniques provide ways to selectively enhance the polarization of target quantum subsystems. However, the cooling in these techniques are bounded. Here we report the first experimental observation of the HBAC cooling bound. We use HBAC to hyperpolarize nuclear spins in diamond. Using two carbon nuclear spins as the source of polarization (reset) and the 14N nuclear spin as the computation bit, we demonstrate that repeating a single cooling step increases the polarization beyond the initial reset polarization and reaches the cooling limit of HBAC. We benchmark the performance of our experiment over a range of variable reset polarization. With the ability to polarize the reset spins to different initial polarizations, we envisage that the proposed model could serve as a test bed for studies on Quantum Thermodynamics.

quant-ph↗

arXiv2015

Quantum information approach to Bose-Einstein condensation of composite bosons

We consider composite bosons (cobosons) comprised of two elementary particles, fermions or bosons, in an entangled state. First, we show that the effective number of cobosons implies the level of correlation between the two constituent particles. For the maximum level of correlation, the effective number of cobosons is the same as the total number of cobosons, which can exhibit the original Bose-Einstein condensation (BEC). In this context, we study a model of BEC for indistinguishable cobosons with a controllable parameter, i.e., entanglement between the two constituent particles. We find that bi-fermions behave in a predictable way, i.e., the effective number of the ground state coboson is an increasing function of entanglement between a pair of constituent fermions. Interestingly, bi-bosons exhibit the opposite behaviour - the effective number of the ground state coboson is a decreasing function of entanglement between a pair of constituent bosons.

quant-ph↗

arXiv2015

Entropic tests of multipartite nonlocality and state-independent contextuality

We introduce a multipartite extension of an information-theoretic distance first introduced in [Nature 341, 119 (1989)]. We use this new distance to derive entropic tests of multipartite nonlocality for three and for an arbitrary even number of qubits as well as a test of state-independent contextuality. In addition, we re-derive the tripartite Mermin inequality and a state-independent non-contextuality inequality by Cabello [Phys. Rev. Lett. 101, 210401 (2008)]. This suggests that the information-theoretic distance approach to multipartite nonlocality and state-independent contextuality can provide a more general treatment of nonclassical correlations than the orthodox approach based on correlation functions.

quant-ph↗

arXiv2014

The Asymptotic Cooling of Heat-Bath Algorithmic Cooling

The purity of quantum states is a key requirement for many quantum applications. Improving the purity is limited by fundamental laws of thermodynamics. Here we are probing the fundamental limits for a natural approach to this problem, namely heat-bath algorithmic cooling(HBAC). The existence of the cooling limit for HBAC techniques was proved by Schulman et al. in, the limit however remained unknown for the past decade. Here for the first time we find this limit. In the context of quantum thermodynamics, this corresponds to the maximum extractable work from the quantum system.

quant-ph↗