---
title: "Soumyajyoti Chatterjee - Project Engineer in Bengaluru | Tal"
description: "Meet Soumyajyoti Chatterjee, Project Engineer @ C-DAC Bangalore | Data Science | Machine Learning | Deep Learning | Python | Statistics | Optimization | M.Tech Operations Research (NIT Durgapur) based in Bengaluru. AI stack includes anthropic apis, antigravity, and claude code. Explore public work..."
image: "https://tal.club/meet/soumyajyoti-chatterjee/share-image"
---

# Soumyajyoti Chatterjee on Tal

## Headline

Project Engineer @ C\-DAC Bangalore \| Data Science \| Machine Learning \| Deep Learning \| Python \| Statistics \| Optimization \| M\.Tech Operations Research \(NIT Durgapur\)

## About

I am a Project Engineer at C\-DAC Bangalore in the Quantum Technology Group, where I work on quantum algorithms and computational methods at the intersection of machine learning and advanced computing\. I hold an M\.Tech in Operations Research from NIT Durgapur, where I developed a strong foundation in statistics, optimization, and machine learning\. My academic training and research experience have shaped my interest in building data\-driven solutions and applying analytical methods to complex real\-world problems\. My core interests lie in Data Science and Machine Learning\. I enjoy working with Python and ML frameworks such as PyTorch and TensorFlow to build and optimize predictive models, analyze data, and extract meaningful insights\. In my current role, I also explore quantum machine learning approaches and algorithmic techniques using tools such as Qiskit, combining ideas from linear algebra, optimization, and machine learning\. I am particularly interested in opportunities in Data Science and Machine Learning where I can apply my analytical background, programming skills, and research experience to develop impactful data\-driven systems\.

## Location

Bengaluru

## AI stack

anthropic apis

antigravity

claude code

claude

gemini

hugging face

## Work experience

Project Engineer at CDACINDIA, Jul 2025 \- Present · 1 yr 3 mos, • Conduct research on quantum machine learning algorithms, focusing on mitigating the quantum state preparation bottleneck in HHL\-based methods\. • Designed and implemented quantum linear regression approaches using HHL\-based state preparation, improving the efficiency of quantum data encoding\. • Developed and simulated quantum circuits using Qiskit and Python, analyzing algorithm performance and computational complexity\. • Applied concepts from machine learning, linear algebra, and optimization to design scalable quantum algorithms\. • Authored and presented the research work “Quantum Linear Regression via HHL\-Driven State Preparation: Mitigating the QML Encoding Bottleneck” at Supercomputing India 2025 \(SCI\-2025\)\.

Project Intern\(Quantum Technology team\) at CDACINDIA, Aug 2024 \- Jul 2025 · 1 yr, Gained in\-depth exposure to advanced quantum computing concepts, including superposition, entanglement, Dirac notation, quantum interference, and quantum gate\-based circuit design\. Performed rigorous implementation and stepwise analysis of the Harrow\-Hassidim\-Lloyd \(HHL\) algorithm for solving linear systems of equations in C\-DAC’s proprietary quantum simulator, Qniverse\. Investigated and benchmarked quantum data encoding schemes—basis encoding, amplitude encoding, and angle encoding—assessing their efficiency, scalability, and impact on algorithmic runtime within quantum circuits\. Designed and validated a novel quantum state encoding technique, derived from a modified HHL framework, aimed at reducing quantum state preparation complexity while preserving the algorithm’s logarithmic runtime advantage\. Demonstrated the application of the HHL algorithm in quantum\-assisted linear regression for well\-conditioned and sparse matrices, highlighting potential quantum speedup in machine learning workflows\. Critically analyzed the algorithm’s dependence on solution precision 𝜖 and identified key challenges related to quantum state preparation bottlenecks and the current limitations of NISQ\-era quantum hardware\. Delivered detailed presentations on advanced quantum algorithms, including Quantum Phase Estimation, Grover’s Algorithm, Shor’s Algorithm, and Quantum Teleportation, emphasizing their role in achieving provable quantum advantage in specific problem domains\.

Data Science Intern at The Sparks Foundation, Jan 2024 \- Feb 2024 · 2 mos, India

## Education

M\.Tech, Operations Research, National Institute of Technology Durgapur

M\.Sc\., Mathematics, The University of Burdwan

## Public links

- github: https://github\.com/SOUMYAJYOTI1234
- medium\.com: https://medium\.com/@@somchatterjej

[View the original profile](https://tal.club/meet/soumyajyoti-chatterjee)

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