arXiv subjects

Amlan Chakrabarti

Publications and source records attributed to Amlan Chakrabarti.

At least 37 records · Page 2Linked to original sources

arXiv2024

TexIm FAST: Text-to-Image Representation for Semantic Similarity Evaluation using Transformers

One of the principal objectives of Natural Language Processing (NLP) is to generate meaningful representations from text. Improving the informativeness of the representations has led to a tremendous rise in the dimensionality and the memory footprint. It leads to a cascading effect amplifying the complexity of the downstream model by increasing its parameters. The available techniques cannot be applied to cross-modal applications such as text-to-image. To ameliorate these issues, a novel Text-to-Image methodology for generating fixed-length representations through a self-supervised Variational Auto-Encoder (VAE) for semantic evaluation applying transformers (TexIm FAST) has been proposed in this paper. The pictorial representations allow oblivious inference while retaining the linguistic intricacies, and are potent in cross-modal applications. TexIm FAST deals with variable-length sequences and generates fixed-length representations with over 75% reduced memory footprint. It enhances the efficiency of the models for downstream tasks by reducing its parameters. The efficacy of TexIm FAST has been extensively analyzed for the task of Semantic Textual Similarity (STS) upon the MSRPC, CNN/ Daily Mail, and XSum data-sets. The results demonstrate 6% improvement in accuracy compared to the baseline and showcase its exceptional ability to compare disparate length sequences such as a text with its summary.

cs.CL↗

arXiv2024

A Survey of Transformer-based Language Models with Focus on Efficiency

The emergence of Transformer-based Large Language Models (LLMs) has substantially augmented the capabilities of Natural Language Processing (NLP), thereby intensifying the demand for computational resources. Therefore, enhancing efficiency based on factors like computational requirements, energy consumption, carbon footprint and financial cost has become a vital area of research. This motivates us to conduct a survey on Transformer-based LLMs in NLP from the perspective of efficiency. In this survey of 312 articles, the efficiency-improvement endeavors have been systematically discussed targeting various aspects such as data curation, model design, model downsizing, and dynamic inferencing. This has been augmented with efficiency considerations in model adaptation strategies like pre-training, fine-tuning, prompt-engineering and Retrieval-Augmented Generation (RAG). Furthermore, a statistical analysis followed by an in-depth evaluation of the efficiency and efficacy of more than 30 renowned NLP models has been performed on 13 evaluation benchmarks. This paper offers valuable insights for researchers, professionals as well as scholars, and explores the trend of research toward sustainable practices in NLP.

cs.CL↗

arXiv2024

Lumbar Spine Tumor Segmentation and Localization in T2 MRI Images Using AI

In medical imaging, segmentation and localization of spinal tumors in three-dimensional (3D) space pose significant computational challenges, primarily stemming from limited data availability. In response, this study introduces a novel data augmentation technique, aimed at automating spine tumor segmentation and localization through AI approaches. Leveraging a fusion of fuzzy c-means clustering and Random Forest algorithms, the proposed method achieves successful spine tumor segmentation based on predefined masks initially delineated by domain experts in medical imaging. Subsequently, a Convolutional Neural Network (CNN) architecture is employed for tumor classification. Moreover, 3D vertebral segmentation and labeling techniques are used to help pinpoint the exact location of the tumors in the lumbar spine. Results indicate a remarkable performance, with 99% accuracy for tumor segmentation, 98% accuracy for tumor classification, and 99% accuracy for tumor localization achieved with the proposed approach. These metrics surpass the efficacy of existing state-of-the-art techniques, as evidenced by superior Dice Score, Class Accuracy, and Intersection over Union (IOU) on class accuracy metrics. This innovative methodology holds promise for enhancing the diagnostic capabilities in detecting and characterizing spinal tumors, thereby facilitating more effective clinical decision-making.

eess.IV↗

arXiv2024

Panoptic Segmentation and Labelling of Lumbar Spine Vertebrae using Modified Attention Unet

Segmentation and labeling of vertebrae in MRI images of the spine are critical for the diagnosis of illnesses and abnormalities. These steps are indispensable as MRI technology provides detailed information about the tissue structure of the spine. Both supervised and unsupervised segmentation methods exist, yet acquiring sufficient data remains challenging for achieving high accuracy. In this study, we propose an enhancing approach based on modified attention U-Net architecture for panoptic segmentation of 3D sliced MRI data of the lumbar spine. Our method achieves an impressive accuracy of 99.5\% by incorporating novel masking logic, thus significantly advancing the state-of-the-art in vertebral segmentation and labeling. This contributes to more precise and reliable diagnosis and treatment planning.

cs.CV↗

arXiv2023

FragQC: An Efficient Quantum Error Reduction Technique using Quantum Circuit Fragmentation

Quantum computers must meet extremely stringent qualitative and quantitative requirements on their qubits in order to solve real-life problems. Quantum circuit fragmentation techniques divide a large quantum circuit into a number of sub-circuits that can be executed on the smaller noisy quantum hardware available. However, the process of quantum circuit fragmentation involves finding an ideal cut that has exponential time complexity, and also classical post-processing required to reconstruct the output. In this paper, we represent a quantum circuit using a weighted graph and propose a novel classical graph partitioning algorithm for selecting an efficient fragmentation that reduces the entanglement between the sub-circuits along with balancing the estimated error in each sub-circuit. We also demonstrate a comparative study over different classical and quantum approaches of graph partitioning for finding such a cut. We present {\it FragQC}, a software tool that cuts a quantum circuit into sub-circuits when its error probability exceeds a certain threshold. With this proposed approach, we achieve an increase of fidelity by 14.83\% compared to direct execution without cutting the circuit, and 8.45\% over the state-of-the-art ILP-based method, for the benchmark circuits.

quant-ph↗

arXiv2023

SoccerKDNet: A Knowledge Distillation Framework for Action Recognition in Soccer Videos

Classifying player actions from soccer videos is a challenging problem, which has become increasingly important in sports analytics over the years. Most state-of-the-art methods employ highly complex offline networks, which makes it difficult to deploy such models in resource constrained scenarios. Here, in this paper we propose a novel end-to-end knowledge distillation based transfer learning network pre-trained on the Kinetics400 dataset and then perform extensive analysis on the learned framework by introducing a unique loss parameterization. We also introduce a new dataset named SoccerDB1 containing 448 videos and consisting of 4 diverse classes each of players playing soccer. Furthermore, we introduce an unique loss parameter that help us linearly weigh the extent to which the predictions of each network are utilized. Finally, we also perform a thorough performance study using various changed hyperparameters. We also benchmark the first classification results on the new SoccerDB1 dataset obtaining 67.20% validation accuracy. Apart from outperforming prior arts significantly, our model also generalizes to new datasets easily. The dataset has been made publicly available at: https://bit.ly/soccerdb1

cs.CV↗

arXiv2023

Fast 3D Volumetric Image Reconstruction from 2D MRI Slices by Parallel Processing

Magnetic Resonance Imaging (MRI) is a technology for non-invasive imaging of anatomical features in detail. It can help in functional analysis of organs of a specimen but it is very costly. In this work, methods for (i) virtual three-dimensional (3D) reconstruction from a single sequence of two-dimensional (2D) slices of MR images of a human spine and brain along a single axis, and (ii) generation of missing inter-slice data are proposed. Our approach helps in preserving the edges, shape, size, as well as the internal tissue structures of the object being captured. The sequence of original 2D slices along a single axis is divided into smaller equal sub-parts which are then reconstructed using edge preserved kriging interpolation to predict the missing slice information. In order to speed up the process of interpolation, we have used multiprocessing by carrying out the initial interpolation on parallel cores. From the 3D matrix thus formed, shearlet transform is applied to estimate the edges considering the 2D blocks along the $Z$ axis, and to minimize the blurring effect using a proposed mean-median logic. Finally, for visualization, the sub-matrices are merged into a final 3D matrix. Next, the newly formed 3D matrix is split up into voxels and marching cubes method is applied to get the approximate 3D image for viewing. To the best of our knowledge it is a first of its kind approach based on kriging interpolation and multiprocessing for 3D reconstruction from 2D slices, and approximately 98.89\% accuracy is achieved with respect to similarity metrics for image comparison. The time required for reconstruction has also been reduced by approximately 70\% with multiprocessing even for a large input data set compared to that with single core processing.

eess.IV↗

arXiv2022

Robust Quantum Arithmetic Operations with Intermediate Qutrits in the NISQ-era

Numerous scientific developments in this NISQ-era (Noisy Intermediate Scale Quantum) have raised the importance for quantum algorithms relative to their conventional counterparts due to its asymptotic advantage. For resource estimates in several quantum algorithms, arithmetic operations are crucial. With resources reported as a number of Toffoli gates or T gates with/without ancilla, several efficient implementations of arithmetic operations, such as addition/subtraction, multiplication/division, square root, etc., have been accomplished in binary quantum systems. More recently, it has been shown that intermediate qutrits may be employed in the ancilla-free frontier zone, enabling us to function effectively there. In order to achieve efficient implementation of all the above-mentioned quantum arithmetic operations with regard to gate count and circuit-depth without T gate and ancilla, we have included an intermediate qutrit method in this paper. Future research aiming at reducing costs while taking into account arithmetic operations for computing tasks might be guided by our resource estimations using intermediate qutrits. Therefore, the enhancements are examined in relation to the fundamental arithmetic circuits. The intermediate qutrit approach necessitates access to higher energy levels, making the design susceptible to errors. We nevertheless demonstrate that the percentage decrease in the probability of error is significant due to the fact that we achieve circuit efficiency by reducing circuit-depth in comparison to qubit-only works.

quant-ph↗

arXiv2022

On Fault Tolerance of Circuits with Intermediate Qutrit-assisted Gate Decomposition

The use of a few intermediate qutrits for efficient decomposition of 3-qubit unitary gates has been proposed, to obtain an exponential reduction in the depth of the decomposed circuit. An intermediate qutrit implies that a qubit is operated as a qutrit in a particular execution cycle. This method, primarily for the NISQ era, treats a qubit as a qutrit only for the duration when it requires access to the state $\ket{2}$ during the computation. In this article, we study the challenges of including fault-tolerance in such a decomposition. We first show that any qubit that requires access to the state $\ket{2}$ at any point in the circuit, must be encoded using a qutrit quantum error correcting code (QECC), thus resulting in a circuit with both qubits and qutrits at the outset. Since qutrits are noisier than qubits, the former is expected to require higher levels of concatenation to achieve a particular accuracy than that for qubit-only decomposition. Next, we derive analytically (i) the number of levels of concatenation required for qubit-qutrit and qubit-only decompositions as a function of the probability of error, and (ii) the criterion for which qubit-qutrit decomposition leads to a lower gate count than qubit-only decomposition. We present numerical results for these two types of decomposition and obtain the situation where qubit-qutrit decomposition excels for the example circuit of the quantum adder by considering different values for quantum hardware-noise and non-transversal implementation of the 2-controlled ternary CNOT gate.

quant-ph↗

arXiv2022

Robust Quantum Circuit for Clique Problem with Intermediate Qudits

Clique problem has a wide range of applications due to its pattern matching ability. There are various formulation of clique problem like $k$-clique problem, maximum clique problem, etc. The $k$-Clique problem, determines whether an arbitrary network has a clique or not whereas maximum clique problem finds the largest clique in a graph. It is already exhibited in the literature that the $k$-clique or maximum clique problem (NP-problem) can be solved in an asymptotically faster manner by using quantum algorithms as compared to the conventional computing. Quantum computing with higher dimensions is gaining popularity due to its large storage capacity and computation power. In this article, we have shown an improved quantum circuit implementation for the $k$-clique problem and maximum clique problem (MCP) with the help of higher-dimensional intermediate temporary qudits for the first time to the best of our knowledge. The cost of state-of-the-art quantum circuit for $k$-clique problem is colossal due to a huge number of $n$-qubit Toffoli gates. We have exhibited an improved cost and depth over the circuit by applying a generalized $n$-qubit Toffoli gate decomposition with intermediate ququarts (4-dimensional qudits).

cs.ET↗

arXiv2022

QuDiet: A Classical Simulation Platform for Qubit-Qudit Hybrid Quantum Systems

In the recent years, numerous research advancements have extended the limit of classical simulation of quantum algorithms. Although, most of the state-of-the-art classical simulators are only limited to binary quantum systems, which restrict the classical simulation of higher-dimensional quantum computing systems. Through recent developments in higher-dimensional quantum computing systems, it is realized that implementing qudits improves the overall performance of a quantum algorithm by increasing memory space and reducing the asymptotic complexity of a quantum circuit. Hence, in this article, we introduce \textbf{QuDiet}, a state-of-the-art user-friendly python-based higher-dimensional quantum computing simulator. \textbf{QuDiet} offers multi-valued logic operations by utilizing generalized quantum gates with an abstraction so that any naive user can simulate qudit systems with ease as compared to the existing ones. We simulate various benchmark quantum circuits in \textbf{QuDiet} and show the considerable speedup in simulation time as compared to the other simulators without loss in precision. Finally, \textbf{QuDiet} provides a full qubit-qudit hybrid quantum simulator package with quantum circuit templates of well-known quantum algorithms for fast prototyping and simulation. The complete code and packages of \textbf{QuDiet} is available at https://github.com/LegacYFTw/QuDiet so that other platforms can incorporate it as a classical simulation option for qubit-qudit hybrid systems to their platforms.

quant-ph↗

arXiv2022

Discrete-time Quantum Walks in Qudit Systems

Quantum walks contribute significantly to developing quantum algorithms and quantum simulations. Here, we introduce a first of its kind one-dimensional quantum walk in the $d$-dimensional quantum domain, where $d>2$, and show its equivalence for circuit realization in an arbitrary finite-dimensional quantum logic for utilizing the advantage of larger state space, which helps to reduce the run-time of the quantum walks as compared to the conventional binary quantum systems. We provide efficient quantum circuits for the implementation of discrete-time quantum walks (DTQW) in one-dimensional position space in any finite-dimensional quantum system when the dimension is odd using an appropriate logical mapping of the position space on which a walker evolves onto the multi-qudit states. With example circuits for various qudit state spaces, we also explore scalability in terms of $n$-qudit $d$-ary quantum systems. Further, the extension of one-dimensional DTQW to $d$-dimensional DTQW using $2d$-dimensional coin space on $d$-dimensional lattice has been studied, where $d>=2$. Thereafter, the circuit design for the implementation of scalable $d$-dimensional DTQW in $d$-ary quantum systems has been portrayed. Lastly, we exhibit the circuit design for the implementation of DTQW using different coins on various search spaces.

quant-ph↗

arXiv2022

Intermediate Qutrit-based Improved Quantum Arithmetic Operations with Application on Financial Derivative Pricing

In some quantum algorithms, arithmetic operations are of utmost importance for resource estimation. In binary quantum systems, some efficient implementation of arithmetic operations like, addition/subtraction, multiplication/division, square root, exponential and arcsine etc. have been realized, where resources are reported as a number of Toffoli gates or T gates with ancilla. Recently it has been demonstrated that intermediate qutrits can be used in place of ancilla, allowing us to operate efficiently in the ancilla-free frontier zone. In this article, we have incorporated intermediate qutrit approach to realize efficient implementation of all the quantum arithmetic operations mentioned above with respect to gate count and circuit-depth without T gate and ancilla. Our resource estimates with intermediate qutrits could guide future research aimed at lowering costs considering arithmetic operations for computational problems. As an application of computational problems, related to finance, are poised to reap the benefit of quantum computers, in which quantum arithmetic circuits are going to play an important role. In particular, quantum arithmetic circuits of arcsine and square root are necessary for path loading using the re-parameterization method, as well as the payoff calculation for derivative pricing. Hence, the improvements are studied in the context of the core arithmetic circuits as well as the complete application of derivative pricing. Since our intermediate qutrit approach requires to access higher energy levels, making the design prone to errors, nevertheless, we show that the percentage decrease in the probability of error is significant owing to the fact that we achieve circuit robustness compared to qubit-only works.

quant-ph↗

arXiv2021

$i$-QER: An Intelligent Approach towards Quantum Error Reduction

Quantum computing has become a promising computing approach because of its capability to solve certain problems, exponentially faster than classical computers. A $n$-qubit quantum system is capable of providing $2^{n}$ computational space to a quantum algorithm. However, quantum computers are prone to errors. Quantum circuits that can reliably run on today's Noisy Intermediate-Scale Quantum (NISQ) devices are not only limited by their qubit counts but also by their noisy gate operations. In this paper, we have introduced $i$-QER, a scalable machine learning-based approach to evaluate errors in a quantum circuit and helps to reduce these without using any additional quantum resources. The $i$-QER predicts possible errors in a given quantum circuit using supervised learning models. If the predicted error is above a pre-specified threshold, it cuts the large quantum circuit into two smaller sub-circuits using an error-influenced fragmentation strategy for the first time to the best of our knowledge. The proposed fragmentation process is iterated until the predicted error reaches below the threshold for each sub-circuit. The sub-circuits are then executed on a quantum device. Classical reconstruction of the outputs obtained from the sub-circuits can generate the output of the complete circuit. Thus, $i$-QER also provides classical control over a scalable hybrid computing approach, which is a combination of quantum and classical computers. The $i$-QER tool is available at https://github.com/SaikatBasu90/i-QER.

quant-ph↗

arXiv2021

Quantum solutions to possible challenges of Blockchain technology

Technological advancements of Blockchain and other Distributed Ledger Techniques (DLTs) promise to provide significant advantages to applications seeking transparency, redundancy, and accountability. Actual adoption of these emerging technologies requires incorporating cost-effective, fast, QoS-enabled, secure, and scalable design. With the recent advent of quantum computing, the security of current blockchain cryptosystems can be compromised to a greater extent. Quantum algorithms like Shor's large integer factorization algorithm and Grover's unstructured database search algorithm can provide exponential and quadratic speedup, respectively, in contrast to their classical counterpart. This can put threats on both public-key cryptosystems and hash functions, which necessarily demands to migrate from classical cryptography to quantum-secure cryptography. Moreover, the computational latency of blockchain platforms causes slow transaction speed, so quantum computing principles might provide significant speedup and scalability in transaction processing and accelerating the mining process. For such purpose, this article first studies current and future classical state-of-the-art blockchain scalability and security primitives. The relevant quantum-safe blockchain cryptosystem initiatives which have been taken by Bitcoin, Ethereum, Corda, etc. are stated and compared with respect to key sizes, hash length, execution time, computational overhead, and energy efficiency. Post Quantum Cryptographic algorithms like Code-based, Lattice-based, Multivariate-based, and other schemes are not well suited for classical blockchain technology due to several disadvantages in practical implementation. Decryption latency, massive consumption of computational resources, and increased key size are few challenges that can hinder blockchain performance.

cs.CR↗

arXiv2021

Qurzon: A Prototype for a Divide and Conquer Based Quantum Compiler

When working with algorithms on quantum devices, quantum memory becomes a crucial bottleneck due to low qubit count in NISQ-era devices. In this context, the concept of `divide and compute', wherein a quantum circuit is broken into several subcircuits and executed separately, while stitching the results of the circuits via classical post-processing, becomes a viable option, especially in NISQ-era devices. This paper introduces \textbf{Qurzon}, a proposed novel quantum compiler that incorporates the marriage of techniques of divide and compute with the state-of-the-art algorithms of optimal qubit placement for executing on real quantum devices. A scheduling algorithm is also introduced within the compiler that can explore the power of distributed quantum computing while paving the way for quantum parallelism for large algorithms. Several benchmark circuits have been executed using the compiler, thereby demonstrating the power of the divide and compute when working with real NISQ-era quantum devices.

quant-ph↗

arXiv2021

Faster Search of Clustered Marked States with Lackadaisical Quantum Walks

The nature of discrete-time quantum walk in the presence of multiple marked states has been studied by Nahimovs and Rivosh. They introduced an exceptional configuration of clustered marked states $i.e.,$ if the marked states are arranged in a $\sqrt{k} \times \sqrt{k}$ cluster within a $\sqrt{N} \times \sqrt{N}$ grid, where $k=n^{2}$ and $n$ an odd integer. They showed that finding a single marked state among the multiple ones using quantum walk with AKR (Ambainis, Kempe and Rivosh) coin requires $\Omega(\sqrt{N} - \sqrt{k})$ time. Furthermore, Nahimov and Rivosh also showed that the Grover's coin can find the same configuration of marked state both faster and with higher probability compared to that with the AKR coin. In this article, we show that using lackadaisical quantum walk, a variant of a three-state discrete-time quantum walk on a line, the success probability of finding all the clustered marked states of this exceptional configuration is nearly 1 with smaller run-time. We also show that the weights of the self-loop suggested for multiple marked states in the state-of-the-art works are not optimal for this exceptional configuration of clustered mark states. We propose a range of weights of the self-loop from which only one can give the desired result for this configuration.

quant-ph↗

arXiv2021

Moving Quantum States without SWAP via Intermediate Higher Dimensional Qudits

Quantum algorithms can be realized in the form of a quantum circuit. To map quantum circuit for specific quantum algorithm to quantum hardware, qubit mapping is an imperative technique based on the qubit topology. Due to the neighbourhood constraint of qubit topology, the implementation of quantum algorithm rightly, is essential for moving information around in a quantum computer. Swapping of qubits using SWAP gate moves the quantum state between two qubits and solves the neighbourhood constraint of qubit topology. Though, one needs to decompose the SWAP gate into three CNOT gates to implement SWAP gate efficiently, but unwillingly quantum cost with respect to gate count and depth increases. In this paper, a new formalism of moving quantum states without using SWAP operation is introduced for the first time to the best of our knowledge. Moving quantum states through qubits have been attained with the adoption of temporary intermediate qudit states. This introduction of intermediate qudit states has exhibited a three times reduction in quantum cost with respect to gate count and approximately two times reduction in respect to circuit depth compared to the state-of-the-art approach of SWAP gate insertion. Further, the proposed approach is generalized to any dimensional quantum system.

quant-ph↗