The reviewed record of science sign in
Pith

arxiv: 2006.04609 · v2 · pith:QBLNMNNA · submitted 2020-06-08 · quant-ph

Experimental Realization of Nonadiabatic Holonomic Single-Qubit Quantum Gates\\ with Optimal Control in a Trapped Ion

Reviewed by Pith T0 review T1 audit T2 compute T3 formal T4 kernel pith:QBLNMNNArecord.jsonopen to challenge →

classification quant-ph
keywords quantumgatesholonomiccomputationcontrolrobustexperimentalfast
0
0 comments X
read the original abstract

Quantum computation with quantum gates induced by geometric phases is regarded as a promising strategy in fault tolerant quantum computation, due to its robustness against operational noises. However, because of the parametric restriction of previous schemes, the main robust advantage of holonomic quantum gates is smeared. Here, we experimentally demonstrate a solution scheme, demonstrating nonadiabatic holonomic single qubit quantum gates with optimal control in a trapped Yb ion based on three level systems with resonant drives, which also hold the advantages of fast evolution and convenient implementation. Compared with corresponding previous geometric gates and conventional dynamic gates, the superiority of our scheme is that it is more robust against control amplitude errors, which is confirmed by the measured gate infidelity through both quantum process tomography and random benchmarking methods. In addition, we also outline that nontrivial two qubit holonomic gates can also be realized within current experimental technologies. Therefore, our experiment validates the feasibility for this robust and fast holonomic quantum computation strategy.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Safe reinforcement learning with online filtering for fatigue-predictive human-robot task planning and allocation in production

    cs.AI 2026-04 unverdicted novelty 5.0

    PF-CD3Q uses online particle filtering to estimate fatigue parameters and constrains a deep Q-learning agent to solve fatigue-aware human-robot task planning as a CMDP.