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.
Speaker: Marin Bukov, UC Berkeley
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