Quantum Chemistry Intern

Position SummaryAs 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 Responsibilities1. 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 SkillsTechnical 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.