Work Experience

May 2025 - Present

Defence Research and Development Organisation - LRDE working on ISAR (Inverse Synthetic Aperture Radar) image classification. My work involves implementing and evaluating advanced deep learning models for classifying radar imagery. Specifically, I have implemented Vision Transformer (ViT), Swin Transformer, and Global Context Vision Transformer architectures from scratch. These transformer-based models are then compared against traditional deep convolutional neural network (DCNN) and CNN architectures to assess performance in terms of accuracy and robustness. The project aims to explore the potential of transformer-based models in enhancing radar image understanding and classification. Computer Vision, Keras, Tensorflow, Data Augmentation

March 2023 - December 2024

IEEE RAS - Junior Coordinator and Core Community At Ras-Manipal, I conducted hands-on training sessions on Arduino kits for a group of over 20 freshers. The sessions covered the basics of Arduino programming, working with sensors, and building simple IoT projects. I guided participants through circuit design, coding in C/C++, and troubleshooting common hardware and software issues. My role focused on helping students understand core electronics concepts while encouraging a collaborative and interactive learning environment. By the end of the training, each participant had built their own working prototype and gained confidence in using microcontrollers for real-world applications. Arduino, C/C++, Sensors, IoT, Embedded Systems

Key Projects

Vision Transformer - Implemented vision transformer , swim transformer , gloabal context vision transformer from scatch for image classification - Computer Vision
GitHub     Documentation
Predicting Objects Motion - Algorithms Applied Such As Kalman Filter , Lucas Kanade Optical Flow , DeepSort / Yolo8n - Computer Vision
GitHub     Documentation
Heart Diesease Prediction - State of the art machine learning model , deployed and monitored using mlflow - Machien learning / mlops
GitHub     Documentation
Lunar Lander Simulation - Implemented using Deep Q-Networks , Vanilla DQN , Rainbow DQN , Monte Carle simulation - Reinforcement Learning
GitHub     Documentation
Fine Tuning LLM - Reinforcement Learning Using Human Feedback - Direct Preference Optimization - Large Language Model
GitHub     Documentation
Implementation of CNN - implementation of convolution neural networks using c++ - Neual Network Architecture
GitHub     Documentation
Sentiment Analysis - Using BERT , RoBERTa and Long Short Term Memory To Perform Sentiment Analyis - Encoder only Architecture
GitHub     Documentation
Image Denoising with U-Net - A convolutional neural network designed to remove noise from images using an encoder-decoder architecture - Convolutional Autoencoder
GitHub     Documentation

Assembly Projects

16-bit RISC-V Processor - A compact 16-bit RISC-V core designed for learning and experimentation . Ideal for FPGA prototyping and embedded applications. - Verilog
GitHub Documentation
Multicycle ARM processor VDHL Implements the ARM instruction set using a control and datapath design. - VDHL
GitHub Documentation

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.

About Me