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 journalArticlepeer-review

14 Citations (Scopus)
60 Downloads (Pure)


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
Issue number9
Early online date24 Apr 2020
Publication statusPublished - 27 Apr 2020

ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics


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