Year: 2026
Type: article
Abstract: Practical and robust control of the complex phases of qubit superposition states is necessary for high-fidelity quantum gates. Here we experimentally demonstrate such control in a trapped ultracold 40 Ca + ion, where the phase of qubit ground state is modified by optical driving to an excited state. Using a third spectator quantum level as reference, we experimentally measure the phases generated on the ground state amplitude by different driving schemes. While geometric phases exhibit intrinsic robustness to systematic control errors due to their dependence on global geometric properties (e.g., Berry curvature) rather than dynamical details, their resilience to decoherence remains a topic requiring critical clarification. In this work, we show that geometric phases are not in general immune to environment-induced decoherence, and their practical robustness, including to decoherence, depends on active error-suppression strategies. We implement different driving schemes and find that minimizing excited state decay is more decisive for their robustness on decoherence than whether the phase is of a geometric or dynamical character. This work established and demonstrated a new control strategy that mitigates decoherence effects in geometric/complex phase systems, compensating for their susceptibility to decoherence. We experimentally benchmark four phase-imprinting protocols under a tunable engineered dissipation channel and controlled systematic errors in a single trapped-ion qubit. Our central quantitative finding is that the sensitivity to decoherence is governed by the time-integrated excited-state population, rather than by whether the accumulated phase is labeled geometric or dynamical.
Source: npj Quantum Information
Institutions: Yanbian University, Zhengzhou University, Henan Academy of Sciences, Chinese Academy of Sciences, Wuhan Institute of Physics and Mathematics, Innovation Academy for Precision Measurement Science and Technology, CAS, University of Chinese Academy of Sciences, Lund University, Institute of Industry Technology Guangzhou
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $4,290
Funders: National Research Foundation, Danmarks Grundforskningsfond, National Natural Science Foundation of China, Knut och Alice Wallenbergs Stiftelse, Special Project for Research and Development in Key areas of Guangdong Province, Wallenberg Centre for Molecular and Translational Medicine, Wallenberg Center for Quantum Technology, Chalmers University of Technology
OpenAlex ID: W7163931734
