Work

Multiqubit and multilevel quantum reinforcement learning with quantum technologies

Year: 2018

Type: article

Abstract: We propose a protocol to perform quantum reinforcement learning with quantum technologies. At variance with recent results on quantum reinforcement learning with superconducting circuits, in our current protocol coherent feedback during the learning process is not required, enabling its implementation in a wide variety of quantum systems. We consider diverse possible scenarios for an agent, an environment, and a register that connects them, involving multiqubit and multilevel systems, as well as open-system dynamics. We finally propose possible implementations of this protocol in trapped ions and superconducting circuits. The field of quantum reinforcement learning with quantum technologies will enable enhanced quantum control, as well as more efficient machine learning calculations.

Source: PLoS ONE

DOI: 10.1371/journal.pone.0200455

Authors: F. A. Cárdenas-López, L. Lamata, J. C. Retamal, E. Solano

Institutions: Universidad de Santiago de Chile, Center for the Development of Nanoscience and Nanotechnology, University of the Basque Country, Ikerbasque, Shanghai University

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,382

Funders: Universidad de Santiago de Chile, Eusko Jaurlaritza, Centro para el Desarrollo de la Nanociencia y la Nanotecnología