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Quantum machine learning with adaptive linear optics

Chabaud, Ulysse and Markham, Damian and Sohbi, Adel (2021) Quantum machine learning with adaptive linear optics. . (Submitted) https://resolver.caltech.edu/CaltechAUTHORS:20210406-152933195

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Abstract

We study supervised learning algorithms in which a quantum device is used to perform a computational subroutine - either for prediction via probability estimation, or to compute a kernel via estimation of quantum states overlap. We design implementations of these quantum subroutines using Boson Sampling architectures in linear optics, supplemented by adaptive measurements. We then challenge these quantum algorithms by deriving classical simulation algorithms for the tasks of output probability estimation and overlap estimation. We obtain different classical simulability regimes for these two computational tasks in terms of the number of adaptive measurements and input photons. In both cases, our results set explicit limits to the range of parameters for which a quantum advantage can be envisaged with adaptive linear optics compared to classical machine learning algorithms: we show that the number of input photons and the number of adaptive measurements cannot be simultaneously small compared to the number of modes. Interestingly, our analysis leaves open the possibility of a near-term quantum advantage with a single adaptive measurement.


Item Type:Report or Paper (Discussion Paper)
Related URLs:
URLURL TypeDescription
http://arxiv.org/abs/2102.04579arXivDiscussion Paper
Additional Information:Presented at AQIS2020. AS has been supported by a KIAS individual grant (CG070301) at Korea Institute for Advanced Study. DM acknowledges support from the ANR through project ANR-17-CE24-0035 VanQuTe.
Funders:
Funding AgencyGrant Number
Korea Institute for Advanced StudyCG070301
Agence Nationale pour la Recherche (ANR)ANR-17-CE24-0035
Record Number:CaltechAUTHORS:20210406-152933195
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20210406-152933195
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:108640
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:07 Apr 2021 23:28
Last Modified:07 Apr 2021 23:28

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