Jun 2025 – Sep 2025 · 3 mos · Seattle, Washington, United States · On-site

Jul 2024 – Aug 2024 · 1 mo · Remote

• Worked on developing quantum secure authentication protocols with privacy protection

May 2024 – Sep 2024 · 4 mos · Remote

• Developed scalable quantum graph neural networks for High Energy Physics analysis at CERN.

Apr 2024 – Aug 2024 · 4 mos · Remote

• Designed quantum convolutional kernels and studied their interpretability and performance for image classification tasks

Jul 2023 – Jan 2024 · 6 mos · Remote

• Optimized the existing tensor network-based QTensor simulator using JAX and enforced scalability by enabling hardware acceleration.

Jun 2023 – Sep 2024 · 1 yr 3 mos · Remote

• Proposed and implemented a quantum simulator for higher and mixed-dimensional quantum systems

Jul 2022 – Sep 2023 · 1 yr 2 mos

• Studied the application of a variant of the Quantum Approximate Optimization Algorithm (QAOA) called Func-QAOA for the integer factorization problem. We proposed a general framework for factorization using FUNC-QAOA that supports advanced factorization techniques by preserving the mathematical structure of the problem. We also attained a quadratic reduction in search space using the modulo approach to find only the smallest factor of a biprime.

May 2022 – Jul 2023 · 1 yr 2 mos · Chennai, Tamil Nadu, India

• Worked on understanding the performance of ReLU neural networks by viewing them as paths of nodes each with a gating signal dependent on the input and an importance value (weight). Demonstrated that individual path learning does not contribute significantly to the performance of a model. Implemented clustering of nodes based on similarity in halfspaces learnt as a means to reduce model complexity.

Jan 2022 – Aug 2022 · 7 mos · Tiruchirappalli, Tamil Nadu, India

• Worked towards building a deep learning model for the action and control module of an advanced driver assistance system using multitask learning. Implemented a SqueezeNet-LSTM architecture with latency in predictions to mimic human driving behaviour. Improved the mean squared error from 0.002 using the SqueezeNet backbone to 0.0009 by incorporating LSTM on the comma.ai driving dataset.

Sep 2021 – Feb 2022 · 5 mos

• Worked in the Blind Quantum Computing (BQC) team to build a software stack for BQC. Main contributions include:
• Porting the Measurement-based Quantum Computing (MBQC) simulator from IBM Qiskit to Amazon Braket
• Implementing a novel converter program to convert any arbitrary quantum circuit to the MBQC model reducing days of manual work to seconds. The converter has also been included in the company's US patent application titled "Simulator for Quantum Computing Systems" - https://patents.google.com/patent/US20230186131A1/en.
• Ideating practical use cases for our BQC stack.

Nov 2020 – Apr 2021 · 5 mos · Tamil Nadu, India

Sep 2020 – May 2023 · 2 yrs 8 mos

Dec 2019 – Mar 2020 · 3 mos · NIT Trichy