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ConQuER SIGNED

Controlling Quantum Experiments with Reinforcement Learning

Total Cost €

EC-Contrib. €

Partnership

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ConQuER project word cloud

Explore the words cloud of the ConQuER project. It provides you a very rough idea of what is the project "ConQuER" about.

source experiments device qubit automatic community niels free experimental characterization create goes invested coming bohr experiment framework efficiency memory ai stabilize quantum time simulations software machine generate power tune multiple abundance data learning tuning intelligence setup rl integration artificial spin copenhagen ing physics precious extract

Project "ConQuER" data sheet

The following table provides information about the project.

Coordinator	KOBENHAVNS UNIVERSITET Organization address address: NORREGADE 10 city: KOBENHAVN postcode: 1165 website: www.ku.dk contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.
Coordinator Country	Denmark [DK]
Total cost	219˙312 €
EC max contribution	219˙312 € (100%)
Programme	1. H2020-EU.1.3.2. (Nurturing excellence by means of cross-border and cross-sector mobility)
Code Call	H2020-MSCA-IF-2019
Funding Scheme	MSCA-IF-EF-RI
Starting year	2020
Duration (year-month-day)	from 2020-09-01 to 2022-08-31

Partnership

Take a look of project's partnership.

#	participants	country	role	EC contrib. [€]
1	KOBENHAVNS UNIVERSITET KOBENHAVNS UNIVERSITET Organization address address: NORREGADE 10 city: KOBENHAVN postcode: 1165 website: www.ku.dk contact info title: n.a. name: n.a. surname: n.a. function: n.a. email: n.a. telephone: n.a. fax: n.a.	DK (KOBENHAVN)	coordinator	219˙312.00

Map

Project objective

This proposal aims to advance the integration of state-of-the-art machine learning and artificial intelligence (AI) with quantum physics experiments. The abundance of physics data coming from experiments and simulations of quantum systems allows us to use the power and efficiency of machine learning methods to extract information from this data in a way that goes beyond traditional methods. Based on this insight, I will design and implement a reinforcement learning (RL) method that can directly control, stabilize (to create a quantum memory) and tune (for device characterization and setup) a multiple-spin-qubit experiment at the Niels Bohr Institute in Copenhagen. The resulting framework and open-source AI software are expected to be useful in any other quantum experiment for which tuning is a major component, and is expected to generate a large impact in the community. Automatic device tuning will free up precious time resources that can be invested in significant experimental advances.

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The information about "CONQUER" are provided by the European Opendata Portal: CORDIS opendata.