Reinforcement learning with a 3D convolutional network designs low-CNOT circuits for variational thermal state preparation of the SYK model up to 14 Majorana fermions, but training uses the exact free energy and fidelity as reward.
Long-range wormhole teleportation
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abstract
We extend the protocol of Gao and Jafferis arXiv:1911.07416 to allow wormhole teleportation between two entangled copies of the Sachdev-Ye-Kitaev (SYK) model communicating only through a classical channel. We demonstrate in finite $N$ simulations that the protocol exhibits the characteristic holographic features of wormhole teleportation discussed and summarized in Jafferis et al. https://www.nature.com/articles/s41586-022-05424-3 . We review and exhibit in detail how these holographic features relate to size winding which, as first shown by Brown et al. arXiv:1911.06314 and Nezami et al. arXiv:2102.01064, encodes a dual description of wormhole teleportation.
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Improving thermal state preparation of Sachdev-Ye-Kitaev model with reinforcement learning on quantum hardware
Reinforcement learning with a 3D convolutional network designs low-CNOT circuits for variational thermal state preparation of the SYK model up to 14 Majorana fermions, but training uses the exact free energy and fidelity as reward.