Quantum Chemistry Intern

Position Summary As a Quantum Chemistry Intern, you will work at the intersection of quantum chemistry, computational chemistry, quantum computing, and AI/ML to accelerate molecular modelling, drug discovery, and materials simulation workflows on next-generation quantum and hybrid quantum-classical platforms built at QpiAI. You will contribute to R&D, algorithm development, benchmark creation, workflow automation, and integration of chemistry engines into the QpiAI quantum stack (classical + quantum). Key Responsibilities 1. Quantum & Computational Chemistry Build, simulate, and analyze molecular systems using ab-initio, DFT, semi-empirical, and post-HF methods. Prepare & run workflows for tasks such as: Geometry optimization Frequency calculations Single-point energies Conformer search PES scans (bond, angle, torsion, R-PES) Interaction energies Benchmark chemical properties across classical software (PySCF, ORCA, Psi4, NWChem, CP2K). Assist in developing molecular datasets and automated pipelines for high-throughput computational studies. Work in the domain of embedding, projection based methodologies, QM/MM and their transferability to Quantum computing domain. Work on advanced methodologies, including: Embedding and projection-based techniques QM/MM (Quantum Mechanics/Molecular Mechanics) approaches Investigate the transferability and application of these advanced methodologies to the domain of Quantum Computing. 2. Quantum Computing for Chemistry Convert molecular Hamiltonians into qubit representations using Jordan-Wigner, Bravyi-Kitaev, Parity mapping, and others. Work on algorithms such as VQE, QITE, QPE, SQD and hybrid variational solvers. Build circuits and ansätze that run efficiently on QPUs and simulators. Perform quantum resource estimation (qubit count, depth, error budgets). Explore quantum-inspired chemical simulation (tensor networks, low-rank factorizations, and others). 3. AI/ML for Chemical Modelling Build ML models for chemical property prediction (GNNs, equivariant networks, transformers for molecules). Work on AI-accelerated tasks such as: Geometry optimization with ML surrogates ML-based PES generation ADMET & physicochemical property prediction Reaction prediction & retrosynthesis models. Integrate ML models with classical + quantum workflows for hybrid solver stacks. Assist in developing machine learning potentials (MLPs) trained on DFT/CC-level data; work includes dataset generation, feature engineering, and model validation. Some ideas about delta - ML will be a plus. Contribute to simulation and data preparation for quantum machine learning (QML) models. 4. Software Development & Integration Develop clean, reusable Python code for molecular workflows and solver pipelines. Integrate computational modules with QpiAI’s software stack. Implement modular APIs for molecule input, visualization, simulation, and post-processing. Experience in running molecular simulations in a high-performance computing environment, version control with Git Contribute to documentation, notebooks, examples, and internal demos. 5. Research, Experimentation & Reporting Conduct literature review on quantum chemistry algorithms, quantum ML, and hybrid workflows. Run experiments, record results, and compare classical vs quantum vs ML performance. Prepare internal reports, technical notes, and presentation material for R&D discussions. Participate in weekly reviews with quantum hardware, algorithms, and AI teams. Required Skills Technical Skills Strong understanding of quantum chemistry (HF, DFT, MP2, CC, PES, orbital theory). Experience with computational chemistry tools (PySCF, ORCA, NWChem, Psi4). Strong Python programming with scientific and cheminformatics libraries (NumPy, SciPy, ASE, RDKit). Familiarity with quantum computing frameworks. Knowledge of ML frameworks (PyTorch/TensorFlow/JAX). Understanding of variational algorithms, quantum Hamiltonians, operator mappings. Domain Knowledge Molecular structure, conformers, basis sets, integrals, spin multiplicity. Reaction chemistry or drug discovery workflows (bonus). Materials properties, band structures, or solid-state methods (bonus). Soft Skills Strong analytical mindset and problem-solving capability. Ability to work in a fast-paced, research-oriented environment. Excellent communication and documentation discipline. Preferred Qualifications Pursuing M.Tech/M.Sc/PhD in Chemistry, Chemical Engineering, Physics, Quantum Computing, or related fields. Prior internships or projects in computational chemistry or quantum algorithms. Publications or preprints in computational chemistry, quantum ML, or quantum algorithms. Hands-on experience with molecular simulation datasets or ML chemical models.