Position Overview We are seeking a Quantum Computational Chemist to lead our molecular simulation efforts. This role focuses on applying quantum computing to complex chemical systems, performing ground-state energy calculations, and generating high-accuracy 2D and 3D Potential Energy Surface (PES) maps. You will translate computational chemistry challenges into quantum circuits to identify novel drug candidates.

Key Responsibilities
• Map and develop molecular structures (e.g., small molecules, transition metal complexes) to qubit Hamiltonians using fermion-to-qubit transformations.
• Run and optimize algorithms like VQE/SQD (using ansätze such as UCCSD or hardware-efficient models) and QPE to simulate chemical properties.
• Plot and analyze Potential Energy Surface (PES) scans and benchmark quantum results against classical computational chemistry methods.
• Utilize active space selection and embedding techniques (QM/MM) to scale simulations for larger molecular targets.
• Work on classical fragmentation techniques to improve scalability of molecular simulations.
• Develop and optimize quantum algorithms for chemical and biological systems.
• Work with computational chemistry and quantum computing packages for quantum chemistry and quantum computing circuit design and simulation.
• Collaborate with AI/ML engineers and computational chemists to model
• Protein–ligand interactions using hybrid quantum-classical approaches.




• Analyze quantum simulation outputs and interpret results in a biological or chemical context.
• Contribute to patent filings, scientific and software documentation. Qualifications Required:
• Master’s degree, PhD, or equivalent industry experience in Quantum Computing/Technology.
• Deep knowledge of electronic structure theory and classical computational chemistry tools (e.g., PySCF, Gaussian).
• Solid foundation in quantum mechanics, quantum information theory, and quantum algorithms.
Qualifications
• Proficiency in Python, with experience using libraries such as Qiskit Ecosystem, Pennylane and CUDA-Q.
• Knowledge of molecular modelling, computational chemistry, or drug discovery pipelines.
• Experience with machine learning, artificial intelligence, and quantum machine learning (QML) is a plus. Preferred:
• Proficiency in Python and scientific computing libraries (NumPy, SciPy, etc.)
• Background in molecular modelling and interpreting results in a practical drug discovery context.
• Solid understanding of quantum algorithms and quantum circuit design.
• Familiarity with hybrid quantum-classical approaches (e.g., VQE, QAOA).
• Hands-on experience with Qiskit, CUDA-Q, Pennylane, or other quantum programming frameworks.
• Experience working with high-performance scientific computing environments is a plus.
• Exposure to machine learning or data science concepts.
• Strong analytical and problem-solving skills.
• Prior research experience or publication in relevant areas is a plus.

📌 QUANTUM COMPUTATIONAL CHEMIST (Secunderabad)
🏢 QClairvoyance Quantum Labs
📍 Secunderabad