Pratiksha Balasaheb Gaikwad

Quantum Chemistry • Scientific Machine Learning • Quantum Simulation • Scientific Software Development

---

About

I am a quantum chemist with experience in electronic-structure theory, scientific machine learning, and exposure to quantum computing from the perspective of quantum simulation for molecular systems. My background combines many-body wavefunction development, DFT-based reaction energetics, and formal training in deep learning and AI methods applied to scientific problems.

During my PhD, I develop geminal and variational wavefunction models and contribute to open-source electronic-structure frameworks (FanPy, PyCI) in Python and C++. My work emphasizes systematic benchmarking against DFT and coupled-cluster references, numerical stability, and reproducible scientific software.

In parallel, I have explored machine learning approaches for molecular energy prediction and surrogate modeling, including training neural networks on large-scale DFT datasets to accelerate structure optimization workflows. I am particularly interested in how data-driven models can complement physics-based methods while preserving reliability and interpretability.

I also maintain an active interest in quantum simulation algorithms, including Hamiltonian encodings and VQE-style methods, with a focus on understanding resource scaling and feasibility for molecular applications.

More broadly, I am motivated by building quantitative, well-validated computational tools and continuously learning at the interface of chemistry, physics, and AI.