Cat-state qubits, a prominent class of bosonic encodings, offer a promising pathway toward hardware-efficient fault-tolerant quantum computing. In this manuscript, we propose an optimally robust control protocol for the cat-state qubits which are stabilized by engineering two-photon dissipation. By deriving an effective two-level description in the cat-state subspace and applying shortcut-to-adiabaticity via inverse engineering, we design a robust protocol to achieve fast and high-fidelity state transfer in the cat-state qubit. We analyze the sensitivity to systematic control errors and identify an optimal robustness condition that strongly suppresses errors induced by imperfections in the driving fields. Furthermore, we show that dissipative confinement efficiently suppresses leakage out of the cat-state subspace caused by the pure dephasing, highlighting an intrinsic advantage of dissipative-cat qubits. This work establishes a robust and leakage-suppressing framework for high-fidelity bosonic qubit control, offering a promising route toward scalable fault-tolerant quantum computing.
Shao-Wei Xu, Zhe-Yuan Zhang, Yi Shi et al.· 0 citations
AI agents are trained on population-scale data to encode broad capabilities spanning those of many practitioners. Yet the artifacts they produce rarely meet the personal bar professionals need to stake their reputation on. On realistic, open-ended tasks where success criteria are heterogeneous and insufficiently documented, individual expertise lives precisely in the elevation and departure from the average. In practice, iterative human-agent interaction surfaces criteria that users cannot fully specify up front, yet apply repeatedly across tasks. We argue this cross-session interaction data is a rich, underused signal for closing the gap to individual expertise. In this work, we propose test-time adaptation through human-agent interaction (TAHI), which integrates these signals into agent context and weights, and crystallizes each user's training and evaluation criteria via an evolving rubric module. We adapt agents to 30 individuals in two high-utility domains, writing and visual creation, on a total of 600 tasks. Our agents improve solo task success by 4.5-20.9% within only tens of tasks. Meanwhile, our evolving rubric module serves as a scalable annotation tool, creating evaluation rubrics that catch 16.0-22.3% more failures than those from LMs or humans alone. While agents are adapted towards individuals, we show these personalized agents also produce improvements in success of up to 8.8% that generalize across users.
Z. Wang, Apurva Gandhi, Rulin Shao et al.· 0 citations
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