Abstract: The ability to prepare a physical system in a desired quantum state is central to many areas of physics, such as nuclear magnetic resonance, quantum simulators, and quantum computing. Yet, preparing states quickly and with high fidelity remains a formidable challenge. I will introduce reinforcement (RL) learning ideas to manipulate quantum states of matter, and explain key practical advantages offered by RL. As a concrete example, I will demonstrate that RL allows to find short, high-fidelity driving protocols for transferring population from an initial to a target state in a non-integrable many-body quantum system of interacting qubits, and a genuinely out-of-equilibrium quantum oscillator. I will highlight the potential usefulness of RL for applications in out-of-equilibrium quantum physics, and discuss potential future applications of RL to periodically-driven systems.
Full details about this meeting will be posted here: https://bids.github.io/MLStatsForum/.
The Machine Learning and Science Forum (formerly the Berkeley Statistics and Machine Learning Forum) meets biweekly to discuss current applications across a wide variety of research domains in the physical sciences and beyond. Hosted by UC Berkeley Physics Professor and BIDS Senior Fellow Uros Seljak, these active sessions bring together domain scientists, statisticians, and computer scientists who are either developing state-of-the-art methods or are interested in applying these methods in their research. All interested members of the UC Berkeley and Berkeley Lab communities are welcome and encouraged to attend. To receive email notifications about upcoming meetings, or to request more information, please contact email@example.com.
Marin Bukov is currently a Gordon and Betty Moore postdoctoral research fellow in the Department of Physics at the University of California, Berkeley. He studies the dynamics of many-body quantum and classical systems away from equilibrium. His research focuses on condensed matter physics, ultracold atoms, statistical mechanics, and the interplay between machine learning and physics.