Skills
Python, Pytorch, Pennylane, Qiskit, DWave, Numpy, Pandas, SciPy, Scikit-learn, Gurobi, CPlex, Machine Learning, Deep Learning, Quantum Computing, Quantum Algorithms, Combinatorial Optimization, QUBO, Quantum AI, Quantum ML, Hybrid Quantum-Classical systems, SQL, C, C++, Git, VQA, VQE, QAOA, PCE, PQC, GPU Programming, LaTeX, Data analysis, Feature Engineering, Rest API, DJango, CI/CD Development, Research, Problem Solving, Critical Thinking, Out-of-the-box Thinking
About
Suman Kumar Roy is a dynamic quantum researcher with 5+ years of industry expertise in hybrid quantum-classical machine learning, variational quantum algorithms, and NISQ-compatible optimization for finance and retail. His core skills span Python, SQL, C, C++ for GPU kernels, quantum frameworks like Pennylane, Qiskit, and D-Wave, plus ML/optimization tools including PyTorch, NumPy, Pandas, Scikit-learn, SciPy, and Gurobi, with strengths in quantum machine learning, QUBO problems, and scalable data pipelines. Key achievements include developing a hybrid quantum forecasting model for 90-day NIFTY50 predictions, engineering QUBO decomposition for 3000-variable problems, implementing Quantum K-Means++, and publishing in arXiv, AIMLSystems 2025, and the International Journal of Imaging Systems, alongside QHack rankings and IBM Quantum certifications. Professionally, he advanced from Senior Analyst at Capgemini (2018-2021), working on building Oracle-to-Azure ETL pipelines via Databricks, ML feature engineering, mentoring juniors, with remote work, to Research Intern (2022) and current Researcher at TCS Research (2023-present), delivering quantum forecasting apps, REST APIs, and decomposition methods for combinatorial optimization problems on Finance and Energy Sector. Proven remote flexibility positions him to drive quantum-ML innovations in distributed teams.
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