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 anstze 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 QpiAIs 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.