High-fidelity geometric gate for silicon-based spin qubits

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

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Author(s)

  • Chengxian Zhang
  • Tao Chen
  • Sai Li
  • Xin Wang
  • Zheng-Yuan Xue

Detail(s)

Original languageEnglish
Article number052302
Journal / PublicationPhysical Review A
Volume101
Issue number5
Online published1 May 2020
Publication statusPublished - May 2020

Abstract

High-fidelity manipulation is key to the physical realization of fault-tolerant quantum computation. Here, we present a protocol to realize universal nonadiabatic geometric gates for silicon-based spin qubits. We find that the advantage of geometric gates over dynamical gates depends crucially on the evolution loop for the construction of the geometric phase. Under appropriate evolution loops, all geometric single- and two-qubit gates can outperform their dynamical counterparts for both systematic and off-resonance noises. We also perform randomized benchmarking using noise amplitudes consistent with experiments in silicon. For the static noise model, the averaged fidelities of geometric gates are around 99.90% or above, while for the time-dependent 1/f -type noise, the fidelities are around 99.98% when only the off-resonance noise is present. We also show that the improvement in fidelities of the geometric gates over dynamical ones typically increases with the exponent α of the 1/f noise, and the ratio can be as high as 4 when α ≈ 3. Our results suggest that geometric gates with judiciously chosen evolution loops can be a powerful way to realize high-fidelity quantum gates.

Research Area(s)

  • QUANTUM-DOT, EXPERIMENTAL REALIZATION, PHASE, MANIPULATION, COHERENCE, NOISE

Citation Format(s)

High-fidelity geometric gate for silicon-based spin qubits. / Zhang, Chengxian; Chen, Tao; Li, Sai; Wang, Xin; Xue, Zheng-Yuan.

In: Physical Review A, Vol. 101, No. 5, 052302, 05.2020.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal