Quantum optical neural networks
Name
s41534-019-0174-7.pdf
Description
Published version
Size
1.64 MB
Format
Adobe PDF
Checksum (MD5)
dec5e2213e7403ccd80c97f994295c6c
Author(s)
Steinbrecher, Gregory R.
Englund, Dirk R.
Carolan, Jacques J
Date Issued
July 2019
Journal
npj Quantum Information
Publisher
Springer Science and Business Media LLC
Citation
Steinbrecher, Gregory R. et al. “Quantum optical neural networks.” npj Quantum Information, 5, 1 (July 2019): 60 © 2019 The Author(s)
Version
Final published version
Abstract
Physically motivated quantum algorithms for specific near-term quantum hardware will likely be the next frontier in quantum information science. Here, we show how many of the features of neural networks for machine learning can naturally be mapped into the quantum optical domain by introducing the quantum optical neural network (QONN). Through numerical simulation and analysis we train the QONN to perform a range of quantum information processing tasks, including newly developed protocols for quantum optical state compression, reinforcement learning, black-box quantum simulation, and one-way quantum repeaters. We consistently demonstrate that our system can generalize from only a small set of training data onto inputs for which it has not been trained. Our results indicate that QONNs are a powerful design tool for quantum optical systems and, leveraging advances in integrated quantum photonics, a promising architecture for next-generation quantum processors.
MIT Department
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Massachusetts Institute of Technology. Research Laboratory of Electronics
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/S41534-019-0174-7
