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

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

Research output: Working paperProfessional

Abstract

Novel computational tools in machine learning open new perspectives in 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 multimodal fiber. We show that by using trainable operators at the input and at the readout, it is possible to realize multi-level gates. We study single and qudit gates, including the scaling properties of the algorithms with the size of the reservoir.
Original languageEnglish
Publication statusPublished - 2019

Fingerprint

machine learning
programming
information systems
readout
optics
scaling
operators
fibers

Cite this

Marcucci, Giulia ; Pierangeli, Davide ; Pinkse, Pepijn W.H. ; Malik, Mehul ; Conti, Claudio. / Programming multi-level quantum gates in disordered computing reservoirs via machine learning. 2019.
@techreport{91cad2b0499940d9abbf4b234e05361b,
title = "Programming multi-level quantum gates in disordered computing reservoirs via machine learning",
abstract = "Novel computational tools in machine learning open new perspectives in 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 multimodal fiber. We show that by using trainable operators at the input and at the readout, it is possible to realize multi-level gates. We study single and qudit gates, including the scaling properties of the algorithms with the size of the reservoir.",
author = "Giulia Marcucci and Davide Pierangeli and Pinkse, {Pepijn W.H.} and Mehul Malik and Claudio Conti",
note = "ArXiv 1905.05264",
year = "2019",
language = "English",
type = "WorkingPaper",

}

Programming multi-level quantum gates in disordered computing reservoirs via machine learning. / Marcucci, Giulia; Pierangeli, Davide; Pinkse, Pepijn W.H.; Malik, Mehul; Conti, Claudio.

2019.

Research output: Working paperProfessional

TY - UNPB

T1 - Programming multi-level quantum gates in disordered computing reservoirs via machine learning

AU - Marcucci, Giulia

AU - Pierangeli, Davide

AU - Pinkse, Pepijn W.H.

AU - Malik, Mehul

AU - Conti, Claudio

N1 - ArXiv 1905.05264

PY - 2019

Y1 - 2019

N2 - Novel computational tools in machine learning open new perspectives in 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 multimodal fiber. We show that by using trainable operators at the input and at the readout, it is possible to realize multi-level gates. We study single and qudit gates, including the scaling properties of the algorithms with the size of the reservoir.

AB - Novel computational tools in machine learning open new perspectives in 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 multimodal fiber. We show that by using trainable operators at the input and at the readout, it is possible to realize multi-level gates. We study single and qudit gates, including the scaling properties of the algorithms with the size of the reservoir.

M3 - Working paper

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

ER -