My Research
Research Interest
I am deeply passionate about research in the interstice of Quantum Information Science, Mathematics and Machine Learning. My research aims to understand what a quantum theory could mean, what it could tell us about nature and how we can use that to build a quantum advantage with machine learning, new mathematical principles, and how it can help us understand complex quantum nature.
I utilize concepts and tools in quantum information theory, machine learning, quantum causal models, quantum software and semi definite programming to formalize and explore new questions spawning from the following directions:
- What quantum nonclassicality means?
- How can we use quantum properties to build quantum advanatge with machine learning algorithms?
- How can a machine learning model equpped with quantum resources learn?
- How can we such models to understand nature, answer foundational questions, and explore problems in quantum chemistry, condensed matter physics, etc.
I aim towards making quantum theory more intuitive to the research community. I am also interested in Science Communication, Interdisciplinary Biology and the Philosophy of Science
I am currently working as a Research Student with Prof. Anil Shaji and Prof. Debashis Saha at IISER(TVM), complementing both analytical techniques & machine learning towards understanding Nonlocality in Quantum Causal Networks. I have been making use of LHV-inspired Machine learning models to explore this domain.
I am interested in the Quantum causal structure approaches to study these correlations, and I have been studying the special case of Quantum Causal Networks, making use of LHV-Neural networks as a witness for non-classical correlations. As part of this journey towards finding the nonlocal distributions I learned Semi definite programming with special emphasis for Non commutative polynomial optimisation problems(NCPOPs). I am also acuainted with other frameworks used to study nonclassical features in causal structures such as Causal Box formalism and Process Matrix formalism. One of my skill that got really polished was on building capable models for capturing quantum phenomena, working with complexity measures such as Hessian matrices for studying the loss landscapes of functions really helped my expertise.
Furthermore, I really honed my skills with Quantum softwares such as Qiskit, Circ, Pennylane, etc. I am experienced with using Tensor Networks such as Matrix Product states on the software. I have also build my experience with Quantum Circuits, Algorithms and Gates, and as part of of this I am experienced with Quantum Machine Learning(qNN, qGAN, qAutoEncoders, etc).