Current project's

Project - Implementing Zero Noise Extrapolation without folding

The two primary requirements in the zero noise extrapolation (ZNE) protocol are the noise factors and the extrapolator. However, the same noise factors does not provide similar accuracy for circuits of different depth and width. And there is no obvio

  • In progress

Project - Quantum Algorithm and Application: Encoding, Simulation and Machine Le

IBM Quantum has been the leader in Quantum Technology in both hardware, middleware, and software. We have latest 156 qubits Heron processor chip which has capability to solve complex problems. Recently, several institutions has show-cased utility sca

  • In progress

Project - Heterogenous Computing In Material Science Research

Compute-intensive Monte Carlo methods are at the heart of material research. This project will explore specific software design patterns for heterogeneous computing specific to material research. The system of interest is dislocation loop models i

  • In progress

Project - Quantum Applications for Hamiltonian Simulations

Apply novel Hamiltonian Simulation Algorithm techniques for applications in chemistry. The focus would be on large-scale models that align with the utility-scale experiments.

  • In progress

Project - Test

test

  • In progress

Project - Multi Agentic Framework for Code Bug Summarisation and Evaluation

The increasing complexity of modern software systems makes error detection and correction challenging for developers, necessitating accurate and insightful error explanations. Generative Large Language Models (LLMs) offer automated solutions but ofte

  • In progress

Project - Robust GraphQL Query Evaluation Pipeline

GraphQL queries are increasingly sophisticated, enabling efficient data retrieval across diverse, nested data structures. However, validating the correctness of these complex queries—particularly those generated by large language models (LLMs)—is a s

  • In progress

Project - Platform for Tabular Data downstream task processing

1. Offer APIs to exercise various types of downstream tasks (like Table-Summary and Table-QA) on Tabular data. 2. Add support (into the platform) for trying with different types of LLMs available on HuggingFace (like IBM Granite models).

  • In progress

How it Work

Mentor and Student do the registration

Menter create the project and student applies on it.

Student Upload the Terms & Conditions and Mentor approves it

Mentor Reviews the Required document uploaded by student

Student submits the project