Optimization of networks for measurement-based quantum computation

TL;DR

This paper presents optimization strategies for continuous variable measurement-based quantum computation, including mitigation of finite squeezing effects and a new scheme that reduces reliance on ancillary cluster states through resource state detection and digital post-processing.

Contribution

It introduces novel optimization methods for MBQC, including mitigation techniques for finite squeezing and a general scheme that minimizes the need for ancillary cluster states.

Findings

Mitigation strategies effectively reduce finite squeezing effects.

A new MBQC scheme reduces reliance on ancillary cluster states.

Optimization recipes improve parameter settings for practical implementation.

Abstract

This work introduces optimization strategies to continuous variable measurement based quantum computation (MBQC) at different levels. We provide a recipe for mitigating the effects of finite squeezing, which affect the production of cluster states and the result of a traditional MBQC. These strategies are readily implementable by several experimental groups. Furthermore, a more general scheme for MBQC is introduced that does not necessarily rely on the use of ancillary cluster states to achieve its aim, but rather on the detection of a resource state in a suitable mode basis followed by digital post-processing. A recipe is provided to optimize the adjustable parameters that are employed within this framework.