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Superconducting Qubits Quantum Machine Learning

Low-noise amplifier cryogenic testbed validation in a TaaS (Testing-as-a-Service) framework

arXiv
Authors: Brandon Boiko, Eric J. Zhang, Doug Jorgesen, Sebastian Engelmann, Curtis Grosskopf, Ryan Paske

Year

2023

Paper ID

55076

Status

Preprint

Abstract Read

~2 min

Abstract Words

248

Citations

N/A

Abstract

As quantum computers based on superconducting qubit processors scale, cryogenic microwave components in the qubit control and readout chain must be appropriately tested and qualified to ensure consistent and high-fidelity quantum computation. However, the intersection of superconducting cryogenics and microwave electronics is a new domain with limited technical and commercial expertise. In this paper we validate a TaaS (testing-as-a-service) framework using an organizational workgroup model that consists of (1) a commercial Test House, (2) standard temperature Component Manufacturer, (3) Academic Partner, and (4) System Integrator to demonstrate a scalable model for the qualification of cryogenic microwave components. The goal of this model is to secure the supply chain and support the rapid growth of Quantum Computing (QC) technologies. The component test vehicle presented in this paper is a low-noise amplifier (LNA) which is a crucial component in the cryogenic chain to ensure adequate signal-to-noise of the qubit readout. We devise standard test metrics and protocols by which LNA performance is measured, including key parameters such as gain and flatness, reflection and isolation, operating bandwidth, and noise figure. We present details of the cryogenic testbed customized for LNA qualification, outline test methodologies, and present a suite of standard processes that are used to systematize data collation and reporting. The testbed is validated by reproducing parameters of a pre-characterized LNA. Its value is demonstrated by characterizing a proof-of-concept cryogenic LNA prototype. Finally, we describe the extension of our TaaS framework toward testing at scale for various active and passive cryogenic components used in QC.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2023 reference point for readers tracking recent quantum research.
  • As quantum computers based on superconducting qubit processors scale, cryogenic microwave components in the qubit control and readout chain must be appropriately tested and...

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