X Center for Quantum Research

X-Center of Quantum Research (XCQR) is the first research division established by the group lead Ankul Prajapati and co-lead by Suyash Gaikwad. Our research is focused on multiple subfields under quantum technology.

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Semiconducting Qubits & Quantum Computing
Semiconducting qubits, as of today,  remain an area of very active research due to their compatibility advantages. We try to develop a mathematical model for qubit dynamics using open quantum systems and Non-Markovian dynamics. Later we plan to apply quantum error correction protocols to control decoherence and see results using special probability distributions.  

Quantum Correlations
In a quantum setting,  elements/states of the system (atoms, qubits, photons, etc) are often interrelated with each other and these relations cannot be found with classical correlations, but quantum correlations fulfill this very job. Quantifying correlations in certain quantum systems and looking for their enhancements mathematically can lead to very promising results with applications.  

Quantum Optics and AMO physics
Light-matter interactions have lead to interesting advancements in various fields after the advent of lasers. Studying these interactions in the quantum regime is of our interest. There has been a lot of theoretical development on the mathematical basis of quantum optics, but computationally exploring some of these areas still leaves us with some open questions, like exploring open quantum systems for a very special set of light-matter systems in a very different physical setting (CQED, Ion Traps, Optical Tweezers, etc). These open questions can lead to some potential work. 

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Quantum Natural Language Processing

Natural language processing (NLP) problems are ubiquitous in classical computing, where they often require millions of dollars of computational resources and billions of parameters to infer sentence meanings. It is not efficient classically to improve these state-of-the-art language models and this means that the required computational resources are expected to scale exponentially with the increase in parameters. With the appearance of quantum computing hardware and simulators, it is worth developing methods to examine such problems on these platforms.  Quantum Natural Language Processing (QNLP) deals with the design and implementation of  NLP  models intended to be run on quantum hardware. We are introducing a version of the popular transformer model in which by application quantum phenomena we are trying to find a way to bring down the training cost in NISQ computers. We are using the DisCoCat language modeling framework to encode text.