Overview
Quantum computing is fast evolving from a research focused towards a market ready technology, with quantum advantage in the near horizon. The algorithms that will lead the charge towards the market unlock are hybrid classical–quantum ones with HPC being at the core of the classical side. In this tutorial, we shall look into two application domains along this — namely combinatorial optimization and quantum chemistry.
Solving combinatorial optimization problems and simulating quantum systems such as molecules and strongly correlated materials are both central challenges in high-performance computing. While classical methods often struggle with exponential scaling, quantum computing offers new algorithmic pathways; however, near-term quantum approaches require careful integration with classical workflows to achieve practical value.
In this tutorial, we present a practical, workflow-oriented view of hybrid quantum–classical computing, focusing on how these approaches can be implemented effectively on real systems. We emphasize what it takes to translate theoretical ideas into practice under realistic constraints such as noise and hardware connectivity. Through guided examples, participants will learn how to design end-to-end workflows that combine quantum circuits with classical and HPC resources, and how to translate these ideas into applications in optimization and simulation. The tutorial emphasizes intuitive understanding and hands-on applicability, making it accessible to students and practitioners looking to get started with quantum-centric supercomputing.
Agenda
| Time (mins) | Topic | Speaker |
|---|---|---|
| 30 | Current landscape of quantum computing | Shesha Raghunathan |
| 80 | Combinatorial optimization on quantum computers | Ritajit Majumdar |
| 80 | Sample-based quantum diagonalization and scalable implementation | Nick Brønn |
| 20 | Synthesis of the workflows and Q&A | All speakers |
Speakers
Ritajit Majumdar
IBM Quantum
Dr. Ritajit Majumdar is a Research Scientist in IBM Quantum’s Quantum Algorithms and Engineering team at IBM India Research Lab, working on large-scale quantum demonstrations. He is joint first author of India’s first potential quantum advantage candidate, cited in the Indian Parliament as the nation’s flagship quantum research. During his PhD at the Indian Statistical Institute, he was a Fulbright-Nehru Doctoral Research Fellow at the IBM T. J. Watson Research Centre. A University of Calcutta Gold Medalist and DST INSPIRE Fellow, he has taught at the University of Calcutta and West Bengal University of Technology.
Nick Brønn
IBM Quantum
Dr. Nick Brønn earned his PhD in experimental condensed matter physics from the University of Illinois in 2013. After developing IBM quantum hardware for seven years, he moved up the stack in 2020 to work with open-source software as his tool for experimenting on quantum computing platforms. This gives him a full-stack perspective that he uses to collaborate on topics spanning superconducting qubits, quantum simulation, quantum machine learning, circuit compilation, and error suppression.
Shesha Raghunathan
IBM Quantum
Dr. Shesha Raghunathan is Strategy Lead for IBM Quantum in India, responsible for quantum hardware deployment and expansion, scaling up quantum adoption, and engaging with academia, industry, research labs, startups and developers. Previously, as Global Lead for Startup Ecosystem, he delivered novel go-to-market functionalities like Qiskit Functions; before that, he worked on Electronic Design Automation (EDA) timing analysis, spanning noise, timing abstraction, reporting, analytics and machine learning. He received his PhD in Quantum Computing from the University of Southern California in 2010 and joined IBM in 2011. His research interests include near-term quantum algorithms, circuit optimization and HPC-Quantum.
Resources
- Slides (coming soon)
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Notebooks / code:
- Sample-based quantum diagonalization (IBM Quantum tutorial)
- More materials will be added soon.
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Further reading:
- Chemistry beyond the scale of exact diagonalization on a quantum-centric supercomputer (Science Advances, 2025)
- Closed-loop calculations of electronic structure on a quantum processor and a classical supercomputer at full scale (Future Generation Computer Systems)
- More resources coming soon.
Contact
For questions about the tutorial, please reach out to the conference organizers.