Develop a scalable hybrid quantum–classical framework for electronic-structure simulation of large and strongly correlated molecular systems using sample-based quantum diagonalization and localized active-space techniques.

Develop a scalable hybrid quantum–classical framework for electronic-structure simulation of large and strongly correlated molecular systems using sample-based quantum diagonalization and localized active-space techniques.

Explore design and implementation of different steps of a compiler for fault-tolerant quantum architectures.

Project Objectives: 1. Dynamic Validation without Ground Truth - Develop methods to validate generated transformations using mined specifications and synthetic test data. - Investigate whether synthetic data and fuzzing can replace explicit

The goal is to explore causal inference in time series and see if foundation models can help in the task.

Integration of profiling into torch.compile workflow with inductor backend, by identifying the following: 1. Which metrics to collect - timing, register access counters, etc 2. Where to insert hooks - Fx graph, Inductor IR, Triton IR, etc 3. Areas

Build notebooks to increase open-source adoption of TSFM models

1. To build an AI-driven evaluation system where AI agents assess and improve the performance of other AI agents. 2. To implement a multi-layer coaching pipeline (Primary Generator → Lightweight Coach → Heavy Coach) for scalable, reliable, self-eval

To produce an embedding model that responds well to user provided instructions