Quantum Information, Quantum Optimal Control and Machine Learning

Hi! I’m Priya, an Integrated PhD student at Indian Institute of Science Education and Research Pune working under the supervision of Prof. T S Mahesh. My PhD is funded by Prime Minister Research Fellowship. My research interest primarily includes Quantum Information, Quantum Optimal Control Theory and Machine learning. Quantum Computers are believed to solve certain computational problem considerably faster than their classical counterparts. Building a quantum computer involves understanding of quantum dynamics. Other than quantum computers, it is vital for a number of applications including spectroscpopy, quantum simulation, chemical kinetics etc.
I try to understand the dynamics of Quantum system under external drive using the tools of quantum control theory. To quantify the parameters of drive, we model it as control problem thus introduce Quatum Optimal control. We made contribution in the field by giving a new algorithm that can be found here.
The other aspect, I’m keenly interested in deals with the intersection of machine learning and quantum information as well as condensed matter physics. The major problem with large quantum system involves the large hilbert space size as the number of qubit increases. It grows exponentially with the number of qubit. Machine learning algorithms are famous to solve the problems with large databases. It has been recently shown by various groups that machine learning algorithms can be employed to solve the quantum many body systems. I’m exploring some of machine learning algorithms for this task. We recently used recommender system, a type of machine learning algorithm, to compute the quantum correlations. The result can be seen here.
I’m maintaining a collection of papers that use machine learning algorithms for quatum physics under the tab Machine Learning meets Quantum Physics