Variational quantum unsampling on a quantum photonic processor
Name
1904.10463.pdf
Description
Submitted version
Size
5.83 MB
Format
Adobe PDF
Checksum (MD5)
75277f6b3bedb4af14339460d151e163
Author(s)
Carolan, Jacques
Mohseni, Masoud
Olson, Jonathan P
Prabhu, Mihika
Chen, Changchen
Bunandar, Darius
Niu, Murphy Yuezhen
Harris, Nicholas C
Wong, Franco NC
Hochberg, Michael
Date Issued
2020
Journal
Nature Physics
Publisher
Springer Science and Business Media LLC
Version
Original manuscript
Abstract
© 2020, The Author(s), under exclusive licence to Springer Nature Limited. A promising route towards the demonstration of near-term quantum advantage (or supremacy) over classical systems relies on running tailored quantum algorithms on noisy intermediate-scale quantum machines. These algorithms typically involve sampling from probability distributions that—under plausible complexity-theoretic conjectures—cannot be efficiently generated classically. Rather than determining the computational features of output states produced by a given physical system, we investigate what features of the generating system can be efficiently learnt given direct access to an output state. To tackle this question, here we introduce the variational quantum unsampling protocol, a nonlinear quantum neural network approach for verification and inference of near-term quantum circuit outputs. In our approach, one can variationally train a quantum operation to unravel the action of an unknown unitary on a known input state, essentially learning the inverse of the black-box quantum dynamics. While the principle of our approach is platform independent, its implementation will depend on the unique architecture of a specific quantum processor. We experimentally demonstrate the variational quantum unsampling protocol on a quantum photonic processor. Alongside quantum verification, our protocol has broad applications, including optimal quantum measurement and tomography, quantum sensing and imaging, and ansatz validation.
MIT Department
Massachusetts Institute of Technology. Research Laboratory of Electronics
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1038/S41567-019-0747-6
