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SUMMARY:Reinforcement learning to manipulate many-body quantum systems - M
 arin Bukov (PKS Dresden)
DTSTART:20230316T140000Z
DTEND:20230316T150000Z
UID:TALK194374@talks.cam.ac.uk
CONTACT:Jan Behrends
DESCRIPTION:Manipulating quantum many-body states is a central milestone e
 n route to harnessing quantum technologies. However\, the exponential grow
 th of the Hilbert space dimension with the number of qubits makes it chall
 enging to classically simulate quantum many-body systems and consequently\
 , to devise reliable and robust optimal control protocols. I will present 
 a novel framework for efficiently controlling quantum many-body systems ba
 sed on deep reinforcement learning (RL). Applications include the design o
 f entangling two-body gates which outperform state-of-the-art pulse sequen
 ces used in superconducting qubits platforms\, and the construction of cir
 cuit-based protocols to prepare ground states of quantum spin chains away 
 from the adiabatic regime using ideas from counter-diabatic driving. To ta
 ckle the quantum many-body control problem\, we leverage matrix product st
 ates (i) for representing the many-body state and\, (ii) as part of the tr
 ainable machine learning architecture for our RL agent. In particular\, we
  demonstrate that RL agents are capable of finding universal controls\, of
  learning how to optimally steer previously unseen many-body states\, and 
 of adapting control protocols on-the-fly when the quantum dynamics is subj
 ect to stochastic perturbations.
LOCATION:TCM Seminar Room
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