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Cooling an Optically Trapped Ultracold Fermi Gas by Periodical Driving
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Gottesman-Kitaev-Preskill State Preparation Using Periodic Driving.

Xanda C Kolesnikow1, Raditya W Bomantara2, Andrew C Doherty1

  • 1Centre for Engineered Quantum Systems, School of Physics, The University of Sydney, Sydney, NSW 2006, Australia.

Physical Review Letters
|April 13, 2024
PubMed
Summary
This summary is machine-generated.

We propose a new method to prepare Gottesman-Kitaev-Preskill (GKP) states for quantum computing. This approach uses engineered Hamiltonians and superconducting circuits, overcoming experimental challenges in quantum error correction.

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Area of Science:

  • Quantum Information Science
  • Quantum Error Correction
  • Continuous Variable Quantum Systems

Background:

  • Gottesman-Kitaev-Preskill (GKP) codes are crucial for mitigating noise in continuous variable quantum systems.
  • Experimentally realizing GKP states presents significant challenges, hindering their application.

Purpose of the Study:

  • To propose a novel and experimentally feasible method for preparing GKP states.
  • To demonstrate that engineered time-periodic Hamiltonians can host GKP states as their Floquet states.

Main Methods:

  • Engineering a time-periodic Hamiltonian using a superconducting circuit (SQUID shunted by a superinductor and capacitor).
  • Utilizing adiabatic tuning of an external magnetic flux drive frequency to prepare GKP Floquet states.
  • Leveraging superconducting circuit parameters like characteristic impedance and quality factor.

Main Results:

  • Prediction of highly squeezed GKP magic states (>11.9 dB or 10.8 dB) preparable on a microsecond timescale.
  • Demonstration of feasibility under realistic conditions, considering quality factors (10^6, 10^5) and typical flux noise rates.

Conclusions:

  • The proposed method offers a practical pathway for generating high-fidelity GKP states.
  • This advancement is significant for building robust continuous variable quantum computers.
  • The engineered Hamiltonian approach provides a scalable solution for quantum error correction.