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Yueyang Min

Publications and source records attributed to Yueyang Min.

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Fault-tolerant quantum computing with a microwave Cat Bus

The scalability of fault-tolerant neutral-atom quantum computers is constrained by the latency of shuttling with optical tweezers, imposing a stringent trade-off between qubit overhead and circuit depth in quantum algorithm compilation. Here we propose a hardware-efficient, shuttling-free architecture that achieves all-to-all connectivity. Remote Rydberg atoms are resonantly entangled through a microwave ``Cat Bus''---a cavity mode autonomously stabilized in a bosonic cat state. The Cat Bus natively supports the highly parallelized execution of one-to-many $\mathrm{CZ}^n$ gates with exponentially suppressed crosstalk. We derive the resulting cat--atom error channel from the underlying interactions and physical constraints. For fault-tolerant operation, we develop a hardware-aware scheduling scheme that exploits the native cat--atom $\mathrm{CZ}^{n}$ gate to construct a syndrome-extraction circuit with minimum depth. We benchmark the architecture using hypergraph-product (HGP) codes and estimate a 180-fold reduction in syndrome-extraction cycle time at $N=10^5$ data qubits compared with an atom-rearrangement-based architecture. Under matched two-qubit depolarizing noise, the corresponding error threshold increases from $0.55\%$ to $0.72\%$. Under the hardware-derived error model, we obtain a threshold of $0.80\%$, corresponding to a threshold cooperativity of $C_{\mathrm{th}}=7.8 \times 10^4$, compatible with experimentally accessible parameters for Rydberg-coupled microwave-cavity systems. By avoiding atom transport, the Cat Bus provides a route towards high-speed, fault-tolerant neutral-atom quantum computation.

quant-ph

Efficient Preparation of Fermionic Superfluids in an Optical Dipole Trap through Reinforcement Learning

We demonstrate a reinforcement learning (RL) based control framework for optimizing evaporative cooling in the preparation of strongly interacting degenerate Fermi gases of Li6. Using a Soft Actor-Critic (SAC) algorithm, the system autonomously explores a high-dimensional parameter space to learn optimal cooling trajectories. Compared to conventional exponential ramps, our method achieves up to 130% improvement in atomic density within a 0.5 second, revealing non-trivial control strategies that balance fast evaporation and thermalization. While our current optimization focuses on the evaporation stage, future integration of other cooling stages, such as grey molasses cooling, could further extend RL to the full preparation pipeline. Our result highlights the promise of RL as a general tool for closed-loop quantum control and automated calibration in complex atomic physics experiments.

cond-mat.quant-gas