Hi I'm Anantha
I'm a research scientist at IISER Trivandrum under Prof. Anil Shaji (QIT Group) and Prof. Debashis Saha (Quantum Foundations Group). I recieved my Bachelor's and Master's as an integrated degree student in 2023 advised by Prof. Shaji and Prof. Saha with strong expertise in Quantum Information Theory and Machine Learning, with my final year specialising in Quantum network correlations. As a Chanakya Fellow, the prestigious felloship for contributions towards quantum technology, I found novel results on the nature of Genuine Network Nonlocality. Currently as a Project Associate my research questions how foundations and general computational advantages can bring exponential speedup to quantum machine learning algorithms.
My core research is Learning, my existing works revolved around using learning algorithms to understand quantum network systems. Now I looking forward unto how foundations of quantum computational advantages and learning theory can build better quantum learning algorithms. My interest also extends to nature inspired metaheursitic learning algorithms and how natural and artifical learning can be both integrated.
Selected Projects & Experiences
Publications and Research Theses
Musing's I find interesting
Classical machine learning greatly supports in understanding quantum nature, is it time yet for its quantum counterpart to enter the picture, or is all research on quantum machine learning basically laying the foundation for the future. Then what in both of these is greatly playing a role in research? How we can learn? and why is it important to understand that? (Not the application of using a ML technique in the quantum field, but what comes before the technique where we show how we can learn this subject, ofcourse the application is incredibly fun, but there is a fundamental connection between applications when we look at how we can learn something and not just applying)
Quantum machine learning is really exciting, but am I missing the greater picture and potential of "learning" theory and algorithm in all the excitement. As a matter of fact ml for quantum computing is incredibly fun but takes the non-standard approach in QML. And all the incredible we can uncover in condensed matter physics is so much fun. So its important to see the practical results also and not just the complex rabbit hole of methodology. So how do I draw the line between impactful research and exciting research?
How's it been?
Looking forward to the QM100 Conference on Foundations oF Quantum Mechanics at IISER Kolkata
I joined as a Project Associate-I at IISER Thiruvananthapuram under the advice of Dr. Debashis Saha and Prof. Anil Shaji
I attended the QCMC24 and presented my paper, Dr. Manik Banik, Dr. Ananda G Maity, Prof. Sibashis, Prof. Carol, Prof. Elham Kashefi, Prof. Mark Wilde it was fun talking and getting to know everyone. Wonderful organizing commitee
I am finishing my work with the GNN in network succesfully developing a noise robust proof for complex quantum structures.
I recieved the prestigious Chanakya PG fellowship from I-HUB Quantum Technology Foundation for my work with Prof. Anil Shaji and Dr. Debashis Saha on Genuine Network Nonlocality in Quantum Multipartite Network Systems
I graduated my Bachelors and Masters Integrated Research Degree from IISER Thiruvanathapuram. Many many thanks to Prof. Anil Shaji and Dr. Debashis Saha and our wonderful quantum information theory group. My specialization is in Quantum Information Theory and Machine Learning. My work specifically focused on creating a LHV Neural network model for exploring Quantum correlations in Quantum Bell experiments