39 citations · 66 across the 7 of their papers we have counts for
7 papers
Efficient Preparation of Fermionic Superfluids in an Optical Dipole Trap through Reinforcement Learning
Yueyang Min, Ziliang Li, Yi Zhong +4
We demonstrate a reinforcement learning (RL) based control framework for optimizing evaporative cooling in the preparation of strongly interacting degenerate Fermi gases of Li6. Us…
Adaptive quantum optimization algorithms for programmable atom-cavity systems
Yuchen Luo, Xiaopeng Li, Jian Lin
Developing quantum algorithms adaptive to specific constraints of near-term devices is an essential step towards practical quantum advantage. In a recent work [Phys. Rev. Lett. 131…
Universal Quantum Optimization with Cold Atoms in an Optical Cavity
Meng Ye, Ye Tian, Jian Lin +6
Cold atoms in an optical cavity have been widely used for quantum simulations of many-body physics, where the quantum control capability has been advancing rapidly in recent years.…
Hard instance learning for quantum adiabatic prime factorization
Jian Lin, Zhengfeng Zhang, Junping Zhang +1
Prime factorization is a difficult problem with classical computing, whose exponential hardness is the foundation of Rivest-Shamir-Adleman (RSA) cryptography. With programmable qua…
Quantum Adiabatic Doping for Atomic Fermi-Hubbard Quantum Simulations
Jue Nan, Jian Lin, Yuchen Luo +2
There have been considerable research efforts devoted to quantum simulations of Fermi-Hubbard model with ultracold atoms loaded in optical lattices. In such experiments, the antife…
Quantum Adiabatic Doping with Incommensurate Optical Lattices
Jian Lin, Jue Nan, Yuchen Luo +2
Quantum simulations of Fermi-Hubbard models have been attracting considerable efforts in the optical lattice research, with the ultracold anti-ferromagnetic atomic phase reached at…
Quantum Adiabatic Algorithm Design using Reinforcement Learning
Jian Lin, Zhong Yuan Lai, Xiaopeng Li
Quantum algorithm design plays a crucial role in exploiting the computational advantage of quantum devices. Here we develop a deep-reinforcement-learning based approach for quantum…