About Me

I am a passionate and driven individual with a deep interest in computer science, machine learning, artificial intelligence, and quantum computing. My curiosity fuels my desire to explore the cutting-edge advancements in these fields, pushing me to continually learn, innovate, and expand my understanding of complex systems. I am fascinated by the potential of intelligent algorithms and quantum technologies to revolutionize industries, solve intricate problems, and redefine the way we interact with the digital world. My goal is to leverage my skills, creativity, and technical expertise to develop groundbreaking solutions that contribute to the evolution of computing. Whether it's designing efficient machine learning models, optimizing quantum algorithms, or exploring novel AI architectures, I am committed to pushing the boundaries of technology and shaping a future driven by intelligent and powerful computational systems. 



  


















LeetCode / CodeForces





    

        LeetCode

        
            LeetCode
        

    

    

        Codeforces

        
            Codeforces
        

    











  

  
Work Experience

  










Research Papers





    


        
            Less is More: Design of Hybrid Convolutional-Attention Networks
        

        

            Hybrid convolutional-attention architecture for ISAR target recognition, combining CNNs and attention mechanisms to improve classification accuracy while maintaining computational efficiency.
            Conference
        


        

            Read Paper
        


    


    


        
            Attention Enhanced U-Net for Radar Target Detection
        

        

            Proposed an attention-enhanced U-Net architecture for robust radar target detection under low signal-to-noise ratio (SNR) conditions.
            Conference
        


        

            Read Paper
        


    























Key Projects





    

        Vision Transformer
        

            Implemented Vision Transformer, Swin Transformer, and Global Context Vision Transformer from scratch for image classification.
            Computer Vision
        

        

            GitHub
            Documentation
        

    


    

        Predicting Objects Motion
        

            Implemented Kalman Filter, Lucas-Kanade Optical Flow, YOLOv8n, and DeepSORT for real-time object motion prediction and tracking.
            Computer Vision
        

        

            GitHub
            Documentation
        

    


    

        Heart Disease Prediction
        

            Built and deployed a machine learning pipeline with MLflow for experiment tracking, deployment, and monitoring.
            Machine Learning / MLOps
        

        

            GitHub
            Documentation
        

    


    

        Lunar Lander Simulation
        

            Implemented Deep Q-Network, Vanilla DQN, Rainbow DQN, and Monte Carlo methods for autonomous lunar lander control.
            Reinforcement Learning
        

        

            GitHub
            Documentation
        

    


    

        Fine-Tuning LLMs
        

            Fine-tuned large language models using Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO).
            Large Language Models
        

        

            GitHub
            Documentation
        

    


    

        Implementation of CNN
        

            Implemented convolutional neural networks from scratch in C++ for image classification and feature extraction tasks.
            Neural Network Architecture
        

        

            GitHub
            Documentation
        

    


    

        Sentiment Analysis
        

            Fine-tuned BERT, RoBERTa, and LSTM models for accurate sentiment classification across diverse natural language datasets.
            Encoder-only Architecture
        

        

            GitHub
            Documentation
        

    


    

        Image Denoising with U-Net
        

            Implemented a U-Net encoder-decoder architecture for image denoising and high-quality image reconstruction from noisy inputs.
            Convolutional Autoencoder
        

        

            GitHub
            Documentation
        

    







    


    

    
 
    













Assembly Projects





    

        16-bit RISC-V Processor

        

            A compact 16-bit RISC-V processor designed for learning and experimentation. Ideal for FPGA prototyping and embedded applications.
            Verilog
        


        

            GitHub
            Documentation
        

    


    

        Multicycle ARM Processor

        

            Implements a multicycle ARM processor using separate control and datapath units, supporting the ARM instruction set architecture.
            VHDL
        


        

            GitHub
            Documentation
        

    





















    

    
    
        
          
        
        
          
        
        
          
        
      
      

        

        

        

        

          

        

        

          

            

              

                
                  
                
              

              
                
                  
                
              
              

                Adi
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                    Predicting Object Motion
                  
Real time object tracking and prediction using kalman filters,Lucas kanade optical flow algorithm and yolo8 with deepsort for motion prediction and multi object detection.

                

              

              

                
                

                  
                    
                  
                

                

                  
Global context vision transformer

                  
Built a global context vision transformer for image classification tasks.

                

              



              

                
                

                  
                    
                  
                


                

                  
swin transformer

                  

                    built a swin transformer for image classification tasks.
                  

                

              


              

                
                

                  
                    
                  
                


                

                  
.Conways game of life

                  

                    built an implementation of conways game of life using python and built and engine to run it on a 3d grid using opengl. 
                  

                

              


              

                
                

                  
                    
                  
                


                

                  
Wave Function Collapse

                  

                    developed a wave function collapse algorithm for procedural generation of 2d tile based maps.
                  

                

              


              

                
                

                  
                    
                  
                


                

                  
A* algorithm implementation

                  

                    implemented the A* pathfinding algorithm for efficient route planning in grid-based environments.
                  

                

              


              

                
                

                  
                    
                  
                


                

                  
.Fine tuning BERT to classify sentiment 

                  

                    fine tuned a pre trained bert model to classify sentiment of text data using pytorch and huggingface transformers library.
                  

                

              


              

                
                

                  
                    
                  
                


                

                  
Neural Networks Built From Scratch in c++ Mathematical Analysis of NN

                  

                    built a neural network from scratch in c++ and performed mathematical analysis of the neural network to understand the working of the neural network. 
                  

                

              




              

                
                

                  
                    
                  
                

                

                  
.Poisonous Mushroom Binary Classification

                  

                    built a binary classification model to classify poisonous and edible mushrooms using scikit learn and pandas.
                  

                

              

              

                
                

                  
                    
                  
                

                

                  

                    Gold Price Prediction
                  

                  

                    built a model to predict the gold price using machine learning algorithms and performed data analysis on the gold price data.
                  

                

              

              

                
                

                  
                    
                  
                

                

                  
heart diesease prediction 

                  

                    built a model to predict heart disease using machine learning algorithms and performed data analysis on the heart disease data.
                  

                

              

            

          

        

      

      








    
 
    





    


      



    

      

        
          
            
          
        
        
View All Repositories

      

    
    

    

    






    
 
    







    


  
      

          

              

                  

                  

                  

              

              Terminal
          

          
         
          

              

          

      



  

  
  
  





































    

    
 
    






    
Quantum Computing Repository


    

        Quantum Computing Repository Circuit
    










 










  

  

  

  GitHub Repository
  Qubit visualizer












  
  
  

  
QUANTUM COMPUTING

  
What is quantum computing?

  
The Quantum Computing Revolution
Quantum computing represents a fundamental shift in computational capability, leveraging quantum mechanical phenomena to process information in ways impossible for classical systems. Unlike binary bits that represent 0 or 1, quantum bits (qubits) exploit superposition to exist in multiple states simultaneously. This unique property enables quantum computers to perform complex calculations exponentially faster than traditional supercomputers, particularly for optimization problems and molecular simulations. Current prototypes from IBM and Google utilize superconducting qubits cooled to near-absolute zero, while startups like IonQ employ trapped ion technology for enhanced stability.
Major tech companies and research institutions are racing to achieve quantum supremacy, the point where quantum systems outperform classical computers on specific tasks. While current quantum processors typically contain fewer than 1000 qubits, advancements in error correction and qubit stability suggest practical applications may emerge within this decade. The 2023 breakthrough in logical qubits - error-corrected qubit clusters - marked a critical step toward reliable computation. However, maintaining quantum coherence beyond milliseconds remains a fundamental challenge, with even cosmic radiation posing interference risks.
Potential Applications and Limitations
Promising quantum computing applications span multiple industries: pharmaceutical companies could simulate molecular interactions for drug discovery, financial institutions might optimize complex portfolios, and logistics firms could revolutionize supply chain management. Quantum cryptography promises unhackable communication through quantum key distribution, while machine learning could see dramatic acceleration through quantum neural networks. DARPA's Quantum Benchmarking Initiative recently identified 37 military applications, from radar optimization to nuclear fusion modeling.
Despite its potential, quantum computing faces substantial hurdles. Error rates increase exponentially with qubit count, necessitating advanced error correction algorithms consuming up to 1000 physical qubits per logical qubit. The NISQ (Noisy Intermediate-Scale Quantum) era prioritizes hybrid quantum-classical approaches, combining conventional HPC with quantum accelerators. Different qubit implementations (superconducting loops, trapped ions, photonic qubits) compete for dominance, each with unique advantages and technical barriers. Photonic quantum computers show particular promise for room-temperature operation but struggle with qubit interaction control.
The Road to Commercial Viability
Hybrid quantum-classical systems currently bridge the gap between existing infrastructure and quantum potential. Cloud-based quantum processing units (QPUs) from AWS Braket and Azure Quantum allow researchers to experiment without physical access to fragile quantum hardware. Major milestones include developing fault-tolerant systems and creating standardized programming languages like Q# and Quipper that abstract quantum physics complexities. The 2024 launch of Europe's first quantum composter - a cryogenic facility for multi-qubit chip testing - highlights growing infrastructure investments.
As qubit counts grow, attention shifts from hardware development to practical algorithm implementation. Shor's algorithm for prime factorization threatens current encryption standards, while Grover's algorithm offers quadratic speedups for database searches. The quantum workforce pipeline expands through specialized education programs like MIT's Quantum Curriculum, though interdisciplinary expertise remains scarce. Governments worldwide have launched billion-dollar initiatives recognizing quantum computing's strategic importance, with the US National Quantum Initiative allocating $1.2 billion through 2026.
Emerging Quantum Ecosystem
The quantum stack now encompasses seven distinct layers: quantum materials, control hardware, qubit architectures, error correction, compiler software, algorithms, and cloud interfaces. Startups like Rigetti and Quantinuum compete with tech giants in developing full-stack solutions. Quantum-as-a-Service (QaaS) models emerge as dominant commercialization pathways, with enterprises paying up to $5,000 per hour for quantum cloud access. Investment surged to $2.35 billion globally in 2023 despite macroeconomic downturns, signaling strong market confidence.
Material science breakthroughs continue to reshape the field: topological qubits with inherent error resistance entered prototype testing in 2024, while diamond vacancy qubits show promise for scalable manufacturing. The quantum internet race intensifies with successful quantum memory experiments storing entangled photon states for over 2 seconds - a critical threshold for network viability. Standardization efforts through IEEE Quantum Initiative aim to establish universal metrics for quantum advantage verification and performance benchmarking.
Ethical and Security Implications
The quantum revolution brings unprecedented security challenges. Cryptographic protocols securing global finance and communications face obsolescence - NIST's post-quantum cryptography standardization project aims to future-proof encryption by 2026. Quantum blockchain concepts and quantum-secure satellites enter testing phases as nations scramble to protect critical infrastructure. Dual-use concerns grow as quantum sensors could enable stealth submarine detection or ultra-precise missile guidance systems, potentially destabilizing global security frameworks.
International collaborations like CERN's Quantum Technology Initiative work to prevent technological monopolies through open-source quantum frameworks. The 2025 Quantum Accord signed by 38 nations establishes export controls and ethical guidelines, balancing innovation with proliferation risks. As quantum computing matures, policymakers face complex decisions regarding quantum intellectual property rights, workforce migration patterns, and equitable access to quantum resources across developing nations.
Industry analysts project quantum computing will create $850 billion in annual value by 2040, though adoption curves remain steep. The technology will likely evolve as a specialized co-processor rather than classical computer replacement, integrated into HPC clusters for specific problem classes. With 72% of Fortune 500 companies now running quantum exploration teams, the race to practical quantum advantage continues accelerating across academic, governmental, and commercial spheres.


















 







  
    
    
  
  
  
    

  
    













 









  


   


       
       
       
       












  


  
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