Sabarikirishwaran Ponnambalam

Skills

Programming & Scientific Computing: Python, PyTorch, TensorFlow/Keras, HPC workflows
Quantum Software: Qiskit, PennyLane and TensorFlow Quantum.
Quantum Algorithms: Data Re-uploading, Hybrid Quantum-Classical Algorithms, VQE.
Quantum Machine Learning: Quantum Time-Series Modeling, Quantum Generative Models, Model Benchmarking, Expressibility & Trainability Analysis.
Quantum Simulation & Computational Physics: Hamiltonian simulation, Ising model, First-Principles method: VASP (DFT).
AI & ML: Scientific Machine Learning, Deep Learning, Training LLMs, Graph Neural Networks (GNNs), Multi-Agent Reinforcement Learning.

Cloud & ML Platforms: AWS, SageMaker, AWS Braket, Azure Quantum, Azure ML

About

I develop hybrid quantum-classical algorithms for accelerating scientific computing, with a focus on quantum simulation, quantum machine learning, and AI-accelerated quantum chemistry. I am currently a PhD researcher at Griffith University, where I work on quantum simulation of phonon mediated high temperature superconductivity from first principles.

My quantum research includes NISQ-friendly QML algorithm development for chaotic time-series forecasting, variational quantum algorithms (VQA), quantum generative models (QGAN), and quantum methods for accelerating first-principle atomistic simulation. I have hands-on experience with Qiskit, PennyLane, VQE, training complex parameterized quantum circuits, IonQ Aria, AWS Braket, VASP together with classical ML/DL frameworks including PyTorch and TensorFlow.

My broader AI/ML experience spans time-series modelling, generative learning, graph neural networks, reinforcement learning, and scientific ML. I am particularly suited to R&D roles at the intersection of quantum algorithms, QML, quantum simulation, scientific computing, and computational physics.