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Trapped Ion Quantum Computing

Large N Matrix Quantum Mechanics as a Quantum Memory

arXiv
Authors: ChunJun Cao, Gong Cheng, Brian Swingle

Year

2022

Paper ID

57304

Status

Preprint

Abstract Read

~2 min

Abstract Words

190

Citations

N/A

Abstract

In this paper, we explore the possibility of building a quantum memory that is robust to thermal noise using large N matrix quantum mechanics models. First, we investigate the gauged SU(N) matrix harmonic oscillator and different ways to encode quantum information in it. By calculating the mutual information between the system and a reference which purifies the encoded information, we identify a transition temperature, Tc, below which the encoded quantum information is protected from thermal noise for a memory time scaling as N2. Conversely, for temperatures higher than Tc, the information is quickly destroyed by thermal noise. Second, we relax the requirement of gauge invariance and study a matrix harmonic oscillator model with only global symmetry. Finally, we further relax even the symmetry requirement and propose a model that consists of a large number N2 of qubits, with interactions derived from an approximate SU(N) symmetry. In both ungauged models, we find that the effects of gauging can be mimicked using an energy penalty to give a similar result for the memory time. The final qubit model also has the potential to be realized in the laboratory.

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  • This paper contributes to the Trapped-Ion Quantum Computing research area in the Quantum Articles archive.
  • It adds a 2022 reference point for readers tracking recent quantum research.
  • In this paper, we explore the possibility of building a quantum memory that is robust to thermal noise using large N matrix quantum mechanics models.

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