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QI: Fachverband Quanteninformation
QI 9: Quantum Machine Learning and Classical Simulability
QI 9.12: Vortrag
Dienstag, 19. März 2024, 12:45–13:00, HFT-FT 101
More efficient exchange-only quantum gates via reinforcement learning — •Violeta N. Ivanova-Rohling1,2,3, Niklas Rohling1, and Guido Burkard1 — 1Department of Physics, University of Konstanz — 2Zukunftskolleg, University of Konstanz — 3Department of Mathematical Foundations of Computer Sciences, IMI, Bulgarian Academy of Sciences
There has recently been rapid progress in the research of spin qubits [1], including the realization of exchange-only qubits [2,3]. Here, we use reinforcement learning to optimize the efficiency of exchange-based pulse sequences that encode the universal two-qubit gates CNOT and CZ with nearest-neighbor interaction for quantum dot arrangements in a chain and in a 2 by 3 grid. We improve on gate sequences currently known in the literature. Specifically, with our reinforcement learning framework, we manage to find a gate sequence encoding CNOT with a shorter total time than the Fong-Wandzura sequence [4] which is currently state of the art. Moreover, the flexibility of our approach makes it applicable for gate-sequence optimization for a variety of desired quantum gates and a variety of different connection topologies.
[1] Burkard, Ladd, Pan, Nichol, Petta, Rev. Mod. Phys. 95, 025003 (2023)
[2] DiVincenzo, Bacon, Kempe, Burkard, Whaley, Nature 408, 339 (2000)
[3] Weinstein et al., Nature 615, 817 (2023)
[4] Fong, Wandzura, Quantum Info. Comput. 11, 1003 (2011)