Programming multi-level quantum gates in disordered computing reservoirs via machine learning

Giulia Marcucci, Davide Pierangeli, Pepijn W.H. Pinkse, Mehul Malik, Claudio Conti*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

1 Downloads (Pure)

Abstract

Novel machine learning computational tools open new perspectives for quantum information systems. Here we adopt the open-source programming library TensorFlow to design multi-level quantum gates, including a computing reservoir represented by a random unitary matrix. In optics, the reservoir is a disordered medium or a multi-modal fiber. We show that trainable operators at the input and the readout enable one to realize multi-level gates. We study various qudit gates, including the scaling properties of the algorithms with the size of the reservoir. Despite an initial low slop learning stage, TensorFlow turns out to be an extremely versatile resource for designing gates with complex media, including different models that use spatial light modulators with quantized modulation levels.

Original languageEnglish
Pages (from-to)14018-14027
Number of pages10
JournalOptics express
Volume28
Issue number9
DOIs
Publication statusPublished - 27 Apr 2020

Fingerprint Dive into the research topics of 'Programming multi-level quantum gates in disordered computing reservoirs via machine learning'. Together they form a unique fingerprint.

  • Cite this