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.