Arit Kumar Bishwas - Academia.edu (original) (raw)

Papers by Arit Kumar Bishwas

Research paper thumbnail of Parts of Speech Tagging in NLP: Runtime Optimization with Quantum Formulation and ZX Calculus

ArXiv, 2020

This paper proposes an optimized formulation of the parts of speech tagging in Natural Language P... more This paper proposes an optimized formulation of the parts of speech tagging in Natural Language Processing with a quantum computing approach and further demonstrates the quantum gate-level runnable optimization with ZX-calculus, keeping the implementation target in the context of Noisy Intermediate Scale Quantum Systems (NISQ). Our quantum formulation exhibits quadratic speed up over the classical counterpart and further demonstrates the implementable optimization with the help of ZX calculus postulates.

Research paper thumbnail of An Investigation of Quantum Deep Clustering Framework with Quantum Deep SVM & Convolutional Neural Network Feature Extractor

ArXiv, 2019

In this paper, we have proposed a deep quantum SVM formulation, and further demonstrated a quantu... more In this paper, we have proposed a deep quantum SVM formulation, and further demonstrated a quantum-clustering framework based on the quantum deep SVM formulation, deep convolutional neural networks, and quantum K-Means clustering. We have investigated the run time computational complexity of the proposed quantum deep clustering framework and compared with the possible classical implementation. Our investigation shows that the proposed quantum version of deep clustering formulation demonstrates a significant performance gain (exponential speed up gains in many sections) against the possible classical implementation. The proposed theoretical quantum deep clustering framework is also interesting & novel research towards the quantum-classical machine learning formulation to articulate the maximum performance.

Research paper thumbnail of A Leap from Randomized to Quantum Clustering with Support Vector Machine - A Computation Complexity Analysis

2020 International Conference for Emerging Technology (INCET), 2020

Supervised machine learning deals with developing complex non-linear models, which can be used la... more Supervised machine learning deals with developing complex non-linear models, which can be used later to predict the output for a known input. Clustering is usually treated as an unsupervised machine learning task, but we can formulate a solution to a clustering problem by using a supervised classification algorithm [1]. However, these classification algorithms are highly computationally intensive in nature, so the overall complexity in designing a clustering solution is often very costly from an implementation point of view. The more data we use, the more computational power is required too. Recent advancements in quantum computing show promising advantages in dealing with this kind of computational issues we face while training a complex machine-learning algorithm. In this paper, we do a theoretical investigation on the runtime complexity of algorithms, from classical to randomized, and then to quantum frameworks, when designing a clustering algorithm. The analysis shows significan...

Research paper thumbnail of Big data classification with quantum multiclass SVM and quantum one-against-all approach

2016 2nd International Conference on Contemporary Computing and Informatics (IC3I)

In this paper, we have proposed a quantum approach for multiclass support vector machines to hand... more In this paper, we have proposed a quantum approach for multiclass support vector machines to handle big data classification. To achieve this goal, we have also developed and implemented a quantum version of the one-against-all algorithm. The proposed approach demonstrates that the big data multiclass classification can be implemented with quantum multiclass support vector machine in logarithmic time complexity on a quantum computer, compared to the classical multiclass support vector machines that can be implemented with polynomial time complexity. Hence, our proposed approach exhibits an exponential speed up in time complexity for big data multiclass classification.

Research paper thumbnail of An investigation on support vector clustering for big data in quantum paradigm

Quantum Information Processing

The support vector clustering algorithm is a well-known clustering algorithm based on support vec... more The support vector clustering algorithm is a well-known clustering algorithm based on support vector machines using Gaussian or polynomial kernels. The classical support vector clustering algorithm works well in general, but its performance degrades when applied on big data. In this paper, we have investigated the performance of support vector clustering algorithm implemented in a quantum paradigm for possible runtime improvements. We have developed and analyzed a quantum version of the support vector clustering algorithm. The proposed approach is based on the quantum support vector machine [1] and quantum kernels (i.e., Gaussian and polynomial). The classical support vector clustering algorithm converges in (2) runtime complexity, where is the number of input objects and is the dimension of the feature space. Our proposed quantum version converges in ~() runtime complexity. The clustering identification phase with adjacency matrix exhibits (√ 3) runtime complexity in the quantum version, whereas the runtime complexity in the classical implementation is (2). The proposed quantum version of the SVM clustering method demonstrates a significant speed-up gain on the overall runtime complexity as compared to the classical counterpart.

Research paper thumbnail of Quantum Supervised Clustering Algorithm for Big Data

2018 3rd International Conference for Convergence in Technology (I2CT), 2018

Supervised clustering is the process of grouping unlabeled objects by using some specific objecti... more Supervised clustering is the process of grouping unlabeled objects by using some specific objectives and any supervised learning technique. In traditional clustering algorithms, the similarity measure is based on some distance finding formulations. In supervised clustering, this similarity measure is trained with the help of asupervised learning method. In this paper, we have designed a quantum supervised clustering algorithm and analyzed the overall runtime complexity of the algorithm. In our approach, we have trained the similarity measure model with quantum support vector machine. This quantum mechanically trained similarity measure model is used in the quantum K-Means algorithm, instead of the traditional distance-based similarity measure functions, for clustering unlabeled objects. Our analysis demonstrates that the proposed approach exhibits exponential speed up when compared to the classical implementation.

Research paper thumbnail of Quantum Algorithm for Sequence Clustering

Research paper thumbnail of Big Data Quantum Support Vector Clustering

ArXiv, 2018

Clustering is a complex process in finding the relevant hidden patterns in unlabeled datasets, br... more Clustering is a complex process in finding the relevant hidden patterns in unlabeled datasets, broadly known as unsupervised learning. Support vector clustering algorithm is a well-known clustering algorithm based on support vector machines and Gaussian kernels. In this paper, we have investigated the support vector clustering algorithm in quantum paradigm. We have developed a quantum algorithm which is based on quantum support vector machine and the quantum kernel (Gaussian kernel and polynomial kernel) formulation. The investigation exhibits approximately exponential speed up in the quantum version with respect to the classical counterpart.

Research paper thumbnail of Parts of Speech Tagging in NLP: Runtime Optimization with Quantum Formulation and ZX Calculus

ArXiv, 2020

This paper proposes an optimized formulation of the parts of speech tagging in Natural Language P... more This paper proposes an optimized formulation of the parts of speech tagging in Natural Language Processing with a quantum computing approach and further demonstrates the quantum gate-level runnable optimization with ZX-calculus, keeping the implementation target in the context of Noisy Intermediate Scale Quantum Systems (NISQ). Our quantum formulation exhibits quadratic speed up over the classical counterpart and further demonstrates the implementable optimization with the help of ZX calculus postulates.

Research paper thumbnail of Performance Engineering for Native Mobile Applications - Strategy, Implementation & Execution

Research paper thumbnail of Gaussian Kernel in Quantum Paradigm

ArXiv, 2017

The Gaussian kernel is a very popular kernel function used in many machine-learning algorithms, e... more The Gaussian kernel is a very popular kernel function used in many machine-learning algorithms, especially in support vector machines (SVM). For nonlinear training instances in machine learning, it often outperforms polynomial kernels in model accuracy. We use Gaussian kernel profoundly in formulating nonlinear classical SVM. In the recent research, P. Rebentrost et.al. discuss a very elegant quantum version of least square support vector machine using the quantum version of polynomial kernel, which is exponentially faster than the classical counterparts. In this paper, we have demonstrated a quantum version of the Gaussian kernel and analyzed its complexity in the context of quantum SVM. Our analysis shows that the computational complexity of the quantum Gaussian kernel is O(\epsilon^(-1)logN) with N-dimensional instances and \epsilon with a Taylor remainder error term |R_m (\epsilon^(-1) logN)|.

Research paper thumbnail of An all-pair quantum SVM approach for big data multiclass classification

Quantum Information Processing, Sep 8, 2018

In this paper, we have discussed a quantum approach for the all-pair multiclass classification pr... more In this paper, we have discussed a quantum approach for the all-pair multiclass classification problem. We have shown that the multiclass support vector machine for big data classification with a quantum all-pair approach can be implemented in logarithm run time complexity on a quantum computer. In an all-pair approach, there is one binary classification problem for each pair of classes, and so there are classifiers for a class problem. As compared to the classical multiclass support vector machine that can be implemented with polynomial run time complexity, our approach exhibits exponential speed up in the quantum version. The quantum all-pair algorithm can be used with other classification algorithms, and a speed up gain can be achieved as compared to their classical counterparts.

Research paper thumbnail of Gaussian kernel in quantum learning

International Journal of Quantum Information

The Gaussian kernel is a very popular kernel function used in many machine learning algorithms, e... more The Gaussian kernel is a very popular kernel function used in many machine learning algorithms, especially in support vector machines (SVMs). It is more often used than polynomial kernels when learning from nonlinear datasets and is usually employed in formulating the classical SVM for nonlinear problems. Rebentrost et al. discussed an elegant quantum version of a least square support vector machine using quantum polynomial kernels, which is exponentially faster than the classical counterpart. This paper demonstrates a quantum version of the Gaussian kernel and analyzes its runtime complexity using the quantum random access memory (QRAM) in the context of quantum SVM. Our analysis shows that the runtime computational complexity of the quantum Gaussian kernel is approximated to [Formula: see text] and even [Formula: see text] when [Formula: see text] and the error [Formula: see text] are small enough to be ignored, where [Formula: see text] is the dimension of the training instances,...

Research paper thumbnail of An all-pair quantum SVM approach for big data multiclass classification

Quantum Information Processing

In this paper, we have discussed a quantum approach for the all-pair multiclass classification pr... more In this paper, we have discussed a quantum approach for the all-pair multiclass classification problem. We have shown that the multiclass support vector machine for big data classification with a quantum all-pair approach can be implemented in logarithm run time complexity on a quantum computer. In an all-pair approach, there is one binary classification problem for each pair of classes, and so there are classifiers for a class problem. As compared to the classical multiclass support vector machine that can be implemented with polynomial run time complexity, our approach exhibits exponential speed up in the quantum version. The quantum all-pair algorithm can be used with other classification algorithms, and a speed up gain can be achieved as compared to their classical counterparts.

Research paper thumbnail of An all-pair quantum SVM approach for big data multiclass classification

Quantum Information Processing, Sep 8, 2018

In this paper, we have discussed a quantum approach for the all-pair multiclass classification pr... more In this paper, we have discussed a quantum approach for the all-pair multiclass classification problem. We have shown that the multiclass support vector machine for big data classification with a quantum all-pair approach can be implemented in logarithm run time complexity on a quantum computer. In an all-pair approach, there is one binary classification problem for each pair of classes, and so there are classifiers for a class problem. As compared to the classical multiclass support vector machine that can be implemented with polynomial run time complexity, our approach exhibits exponential speed up in the quantum version. The quantum all-pair algorithm can be used with other classification algorithms, and a speed up gain can be achieved as compared to their classical counterparts.

Research paper thumbnail of Parts of Speech Tagging in NLP: Runtime Optimization with Quantum Formulation and ZX Calculus

ArXiv, 2020

This paper proposes an optimized formulation of the parts of speech tagging in Natural Language P... more This paper proposes an optimized formulation of the parts of speech tagging in Natural Language Processing with a quantum computing approach and further demonstrates the quantum gate-level runnable optimization with ZX-calculus, keeping the implementation target in the context of Noisy Intermediate Scale Quantum Systems (NISQ). Our quantum formulation exhibits quadratic speed up over the classical counterpart and further demonstrates the implementable optimization with the help of ZX calculus postulates.

Research paper thumbnail of An Investigation of Quantum Deep Clustering Framework with Quantum Deep SVM & Convolutional Neural Network Feature Extractor

ArXiv, 2019

In this paper, we have proposed a deep quantum SVM formulation, and further demonstrated a quantu... more In this paper, we have proposed a deep quantum SVM formulation, and further demonstrated a quantum-clustering framework based on the quantum deep SVM formulation, deep convolutional neural networks, and quantum K-Means clustering. We have investigated the run time computational complexity of the proposed quantum deep clustering framework and compared with the possible classical implementation. Our investigation shows that the proposed quantum version of deep clustering formulation demonstrates a significant performance gain (exponential speed up gains in many sections) against the possible classical implementation. The proposed theoretical quantum deep clustering framework is also interesting & novel research towards the quantum-classical machine learning formulation to articulate the maximum performance.

Research paper thumbnail of A Leap from Randomized to Quantum Clustering with Support Vector Machine - A Computation Complexity Analysis

2020 International Conference for Emerging Technology (INCET), 2020

Supervised machine learning deals with developing complex non-linear models, which can be used la... more Supervised machine learning deals with developing complex non-linear models, which can be used later to predict the output for a known input. Clustering is usually treated as an unsupervised machine learning task, but we can formulate a solution to a clustering problem by using a supervised classification algorithm [1]. However, these classification algorithms are highly computationally intensive in nature, so the overall complexity in designing a clustering solution is often very costly from an implementation point of view. The more data we use, the more computational power is required too. Recent advancements in quantum computing show promising advantages in dealing with this kind of computational issues we face while training a complex machine-learning algorithm. In this paper, we do a theoretical investigation on the runtime complexity of algorithms, from classical to randomized, and then to quantum frameworks, when designing a clustering algorithm. The analysis shows significan...

Research paper thumbnail of Big data classification with quantum multiclass SVM and quantum one-against-all approach

2016 2nd International Conference on Contemporary Computing and Informatics (IC3I)

In this paper, we have proposed a quantum approach for multiclass support vector machines to hand... more In this paper, we have proposed a quantum approach for multiclass support vector machines to handle big data classification. To achieve this goal, we have also developed and implemented a quantum version of the one-against-all algorithm. The proposed approach demonstrates that the big data multiclass classification can be implemented with quantum multiclass support vector machine in logarithmic time complexity on a quantum computer, compared to the classical multiclass support vector machines that can be implemented with polynomial time complexity. Hence, our proposed approach exhibits an exponential speed up in time complexity for big data multiclass classification.

Research paper thumbnail of An investigation on support vector clustering for big data in quantum paradigm

Quantum Information Processing

The support vector clustering algorithm is a well-known clustering algorithm based on support vec... more The support vector clustering algorithm is a well-known clustering algorithm based on support vector machines using Gaussian or polynomial kernels. The classical support vector clustering algorithm works well in general, but its performance degrades when applied on big data. In this paper, we have investigated the performance of support vector clustering algorithm implemented in a quantum paradigm for possible runtime improvements. We have developed and analyzed a quantum version of the support vector clustering algorithm. The proposed approach is based on the quantum support vector machine [1] and quantum kernels (i.e., Gaussian and polynomial). The classical support vector clustering algorithm converges in (2) runtime complexity, where is the number of input objects and is the dimension of the feature space. Our proposed quantum version converges in ~() runtime complexity. The clustering identification phase with adjacency matrix exhibits (√ 3) runtime complexity in the quantum version, whereas the runtime complexity in the classical implementation is (2). The proposed quantum version of the SVM clustering method demonstrates a significant speed-up gain on the overall runtime complexity as compared to the classical counterpart.

Research paper thumbnail of Quantum Supervised Clustering Algorithm for Big Data

2018 3rd International Conference for Convergence in Technology (I2CT), 2018

Supervised clustering is the process of grouping unlabeled objects by using some specific objecti... more Supervised clustering is the process of grouping unlabeled objects by using some specific objectives and any supervised learning technique. In traditional clustering algorithms, the similarity measure is based on some distance finding formulations. In supervised clustering, this similarity measure is trained with the help of asupervised learning method. In this paper, we have designed a quantum supervised clustering algorithm and analyzed the overall runtime complexity of the algorithm. In our approach, we have trained the similarity measure model with quantum support vector machine. This quantum mechanically trained similarity measure model is used in the quantum K-Means algorithm, instead of the traditional distance-based similarity measure functions, for clustering unlabeled objects. Our analysis demonstrates that the proposed approach exhibits exponential speed up when compared to the classical implementation.

Research paper thumbnail of Quantum Algorithm for Sequence Clustering

Research paper thumbnail of Big Data Quantum Support Vector Clustering

ArXiv, 2018

Clustering is a complex process in finding the relevant hidden patterns in unlabeled datasets, br... more Clustering is a complex process in finding the relevant hidden patterns in unlabeled datasets, broadly known as unsupervised learning. Support vector clustering algorithm is a well-known clustering algorithm based on support vector machines and Gaussian kernels. In this paper, we have investigated the support vector clustering algorithm in quantum paradigm. We have developed a quantum algorithm which is based on quantum support vector machine and the quantum kernel (Gaussian kernel and polynomial kernel) formulation. The investigation exhibits approximately exponential speed up in the quantum version with respect to the classical counterpart.

Research paper thumbnail of Parts of Speech Tagging in NLP: Runtime Optimization with Quantum Formulation and ZX Calculus

ArXiv, 2020

This paper proposes an optimized formulation of the parts of speech tagging in Natural Language P... more This paper proposes an optimized formulation of the parts of speech tagging in Natural Language Processing with a quantum computing approach and further demonstrates the quantum gate-level runnable optimization with ZX-calculus, keeping the implementation target in the context of Noisy Intermediate Scale Quantum Systems (NISQ). Our quantum formulation exhibits quadratic speed up over the classical counterpart and further demonstrates the implementable optimization with the help of ZX calculus postulates.

Research paper thumbnail of Performance Engineering for Native Mobile Applications - Strategy, Implementation & Execution

Research paper thumbnail of Gaussian Kernel in Quantum Paradigm

ArXiv, 2017

The Gaussian kernel is a very popular kernel function used in many machine-learning algorithms, e... more The Gaussian kernel is a very popular kernel function used in many machine-learning algorithms, especially in support vector machines (SVM). For nonlinear training instances in machine learning, it often outperforms polynomial kernels in model accuracy. We use Gaussian kernel profoundly in formulating nonlinear classical SVM. In the recent research, P. Rebentrost et.al. discuss a very elegant quantum version of least square support vector machine using the quantum version of polynomial kernel, which is exponentially faster than the classical counterparts. In this paper, we have demonstrated a quantum version of the Gaussian kernel and analyzed its complexity in the context of quantum SVM. Our analysis shows that the computational complexity of the quantum Gaussian kernel is O(\epsilon^(-1)logN) with N-dimensional instances and \epsilon with a Taylor remainder error term |R_m (\epsilon^(-1) logN)|.

Research paper thumbnail of An all-pair quantum SVM approach for big data multiclass classification

Quantum Information Processing, Sep 8, 2018

In this paper, we have discussed a quantum approach for the all-pair multiclass classification pr... more In this paper, we have discussed a quantum approach for the all-pair multiclass classification problem. We have shown that the multiclass support vector machine for big data classification with a quantum all-pair approach can be implemented in logarithm run time complexity on a quantum computer. In an all-pair approach, there is one binary classification problem for each pair of classes, and so there are classifiers for a class problem. As compared to the classical multiclass support vector machine that can be implemented with polynomial run time complexity, our approach exhibits exponential speed up in the quantum version. The quantum all-pair algorithm can be used with other classification algorithms, and a speed up gain can be achieved as compared to their classical counterparts.

Research paper thumbnail of Gaussian kernel in quantum learning

International Journal of Quantum Information

The Gaussian kernel is a very popular kernel function used in many machine learning algorithms, e... more The Gaussian kernel is a very popular kernel function used in many machine learning algorithms, especially in support vector machines (SVMs). It is more often used than polynomial kernels when learning from nonlinear datasets and is usually employed in formulating the classical SVM for nonlinear problems. Rebentrost et al. discussed an elegant quantum version of a least square support vector machine using quantum polynomial kernels, which is exponentially faster than the classical counterpart. This paper demonstrates a quantum version of the Gaussian kernel and analyzes its runtime complexity using the quantum random access memory (QRAM) in the context of quantum SVM. Our analysis shows that the runtime computational complexity of the quantum Gaussian kernel is approximated to [Formula: see text] and even [Formula: see text] when [Formula: see text] and the error [Formula: see text] are small enough to be ignored, where [Formula: see text] is the dimension of the training instances,...

Research paper thumbnail of An all-pair quantum SVM approach for big data multiclass classification

Quantum Information Processing

In this paper, we have discussed a quantum approach for the all-pair multiclass classification pr... more In this paper, we have discussed a quantum approach for the all-pair multiclass classification problem. We have shown that the multiclass support vector machine for big data classification with a quantum all-pair approach can be implemented in logarithm run time complexity on a quantum computer. In an all-pair approach, there is one binary classification problem for each pair of classes, and so there are classifiers for a class problem. As compared to the classical multiclass support vector machine that can be implemented with polynomial run time complexity, our approach exhibits exponential speed up in the quantum version. The quantum all-pair algorithm can be used with other classification algorithms, and a speed up gain can be achieved as compared to their classical counterparts.

Research paper thumbnail of An all-pair quantum SVM approach for big data multiclass classification

Quantum Information Processing, Sep 8, 2018

In this paper, we have discussed a quantum approach for the all-pair multiclass classification pr... more In this paper, we have discussed a quantum approach for the all-pair multiclass classification problem. We have shown that the multiclass support vector machine for big data classification with a quantum all-pair approach can be implemented in logarithm run time complexity on a quantum computer. In an all-pair approach, there is one binary classification problem for each pair of classes, and so there are classifiers for a class problem. As compared to the classical multiclass support vector machine that can be implemented with polynomial run time complexity, our approach exhibits exponential speed up in the quantum version. The quantum all-pair algorithm can be used with other classification algorithms, and a speed up gain can be achieved as compared to their classical counterparts.