Quantum Lunch Series Event – Speaker Mekena Metcalf

Talk Title: Quantum Algorithms and Reinforcement Learning for Open Quantum System Simulation

Speaker:  Dr. Mekena Metcalf, is a Postdoctoral Fellow in the Computational Research Division at Lawrence Berkeley National Laboratory.

She received her Ph.D. from the University of California, Merced. Dr. Metcalf was the first graduate student in Prof. Chih-Chun Chien’s theory group, and she was in the first cohort of doctoral students in the UC Merced Physics Department. Dr. Metcalf has published numerous theory papers relating to the simulation of quantum matter. In graduate school, she devised methods to detect topological states of ultra-cold atoms and demonstrated novel properties of non-equilibrium fermion dynamics. After receiving her doctoral degree, Dr. Metcalf began developing quantum algorithms at Berkeley Lab. She has developed original quantum algorithms for open-quantum system dynamics and fermion simulation. Dr. Metcalf now leads a team of scientists and engineers developing artificial intelligence algorithms to enhance quantum network and computing control. She is currently the Principal Investigator on a quantum transduction project and a Quantum System Accelerator Project Lead for robust quantum control. Dr. Metcalf is the first postdoc in Computing Sciences to become a Principal Investigator on an LDRD project. She is a member of the Diversity, Equity, and Inclusion working group within the Quantum System Accelerator. Along with her scientific research, Dr. Metcalf is committed to making an inclusive and diverse quantum technology ecosystem a reality.

Abstract:

Undesired interactions with the environment make current quantum technology noisy. Characterizing, modeling, and simulating these system-environment interactions is challenging due to the macroscopic nature of the environment. We designed a quantum algorithm that uses time-dependent auxiliary qubits to mimic a macroscopic environment. The algorithm prepares finite-temperature states of general Hamiltonians; we benchmarked the algorithm performance using the Transverse Field Ising Model.  Our algorithmic approach offers a path towards open-quantum system simulation using quantum computers.

In parallel to quantum algorithm development, we are developing deep reinforcement learning algorithms (DRL) to optimally control quantum systems in the presence of an environment. Artificial intelligence can enhance this efficiency by bridging experimental data and theoretical models. Our DRL algorithms are designed to enhance the efficiency of optomechanical transduction. We are building a device simulator to train the DRL algorithm for a proof-of-principle demonstration. I will discuss current developments and prospects for our quantum artificial intelligence research efforts.

This is a Virtual Event.

For more information email:

quantumcomputing@ncsu.edu