38 citations · 65 across the 16 of their papers we have counts for
16 papers
Reducing Complexity for Quantum Approaches in Train Load Optimization
Zhijie Tang, Albert Nieto-Morales, Arit Kumar Bishwas
Efficiently planning container loads onto trains is a computationally challenging combinatorial optimization problem, central to logistics and supply chain management. A primary so…
One Supervisor, Many Modalities: Adaptive Tool Orchestration for Autonomous Queries
Mayank Saini, Arit Kumar Bishwas
We present an agentic AI framework for autonomous multimodal query processing that coordinates specialized tools across text, image, audio, video, and document modalities. A centra…
Quantum-Secure-By-Construction (QSC): A Paradigm Shift For Post-Quantum Agentic Intelligence
Arit Kumar Bishwas, Mousumi Sen, Albert Nieto-Morales +1
As agentic artificial intelligence systems scale across globally distributed and long lived infrastructures, secure and policy compliant communication becomes a fundamental systems…
A framework to evaluate the performance of Variational Quantum Algorithms
Ernesto Mamedaliev, Vladyslav Libov, Albert Nieto-Morales +2
Variational Quantum Algorithms (VQAs) are promising methods for solving combinatorial optimization problems on noisy intermediate-scale quantum (NISQ) devices. However, benchmarkin…
Towards Resource-Efficient Multimodal Intelligence: Learned Routing among Specialized Expert Models
Mayank Saini, Arit Kumar Bishwas
As AI moves beyond text, large language models (LLMs) increasingly power vision, audio, and document understanding; however, their high inference costs hinder real-time, scalable d…
Quantum-enabled framework for the Advanced Encryption Standard in the post-quantum era
Albert Nieto-Morales, Arit Kumar Bishwas, Joel Jacob Varghese
The Advanced Encryption Standard (AES)-256 remains resistant to known quantum key-search attacks when keys are uniformly generated. We present quantum-enabled AES (QE-AES), a key-m…
Strategic Roadmap for Quantum- Resistant Security: A Framework for Preparing Industries for the Quantum Threat
Arit Kumar Bishwas, Mousumi Sen
As quantum computing continues to advance, its ability to compromise widely used cryptographic systems projects a significant challenge to modern cybersecurity. This paper outlines…
Comprehensive Study on Performance Evaluation and Optimization of Model Compression: Bridging Traditional Deep Learning and Large Language Models
Aayush Saxena, Arit Kumar Bishwas, Ayush Ashok Mishra +1
Deep learning models have achieved tremendous success in most of the industries in recent years. The evolution of these models has also led to an increase in the model size and ene…
Investigation into the Potential of Parallel Quantum Annealing for Simultaneous Optimization of Multiple Problems: A Comprehensive Study
Arit Kumar Bishwas, Anuraj Som, Saurabh Choudhary
Parallel Quantum Annealing is a technique to solve multiple optimization problems simultaneously. Parallel quantum annealing aims to optimize the utilization of available qubits on…
Comparative analysis of diverse methodologies for portfolio optimization leveraging quantum annealing techniques
Zhijie Tang, Alex Lu Dou, Arit Kumar Bishwas
Portfolio optimization (PO) is extensively employed in financial services to assist in achieving investment objectives. By providing an optimal asset allocation, PO effectively bal…
Molecular unfolding formulation with enhanced quantum annealing approach
Arit Kumar Bishwas, Arish Pitchai, Anuraj Som
Molecular docking is a crucial phase in drug discovery, involving the precise determination of the optimal spatial arrangement between two molecules when they bind. The such analys…
Parts of Speech Tagging in NLP: Runtime Optimization with Quantum Formulation and ZX Calculus
Arit Kumar Bishwas, Ashish Mani, Vasile Palade
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 ga…
An Investigation of Quantum Deep Clustering Framework with Quantum Deep SVM & Convolutional Neural Network Feature Extractor
Arit Kumar Bishwas, Ashish Mani, Vasile Palade
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 convolutiona…
An Investigation on Support Vector Clustering for Big Data in Quantum Paradigm
Arit Kumar Bishwas, Ashish Mani, Vasile Palade
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 clu…
Gaussian Kernel in Quantum Learning
Arit Kumar Bishwas, Ashish Mani, Vasile Palade
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…
An All-Pair Quantum SVM Approach for Big Data Multiclass Classification
Arit Kumar Bishwas, Ashish Mani, Vasile Palade
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 classi…