Search Results for author: Hans J. Briegel

Found 24 papers, 4 papers with code

Quantum machine learning beyond kernel methods

1 code implementation25 Oct 2021 Sofiene Jerbi, Lukas J. Fiderer, Hendrik Poulsen Nautrup, Jonas M. Kübler, Hans J. Briegel, Vedran Dunjko

Machine learning algorithms based on parametrized quantum circuits are a prime candidate for near-term applications on noisy quantum computers.

Parametrized quantum policies for reinforcement learning

no code implementations NeurIPS 2021 Sofiene Jerbi, Casper Gyurik, Simon C. Marshall, Hans J. Briegel, Vedran Dunjko

With the advent of real-world quantum computing, the idea that parametrized quantum computations can be used as hypothesis families in a quantum-classical machine learning system is gaining increasing traction.

reinforcement-learning

Collective defense of honeybee colonies: experimental results and theoretical modeling

no code implementations14 Oct 2020 Andrea López-Incera, Morgane Nouvian, Katja Ried, Thomas Müller, Hans J. Briegel

Social insect colonies routinely face large vertebrate predators, against which they need to mount a collective defense.

Development of swarm behavior in artificial learning agents that adapt to different foraging environments

no code implementations1 Apr 2020 Andrea López-Incera, Katja Ried, Thomas Müller, Hans J. Briegel

Collective behavior, and swarm formation in particular, has been studied from several perspectives within a large variety of fields, ranging from biology to physics.

Operationally meaningful representations of physical systems in neural networks

2 code implementations2 Jan 2020 Hendrik Poulsen Nautrup, Tony Metger, Raban Iten, Sofiene Jerbi, Lea M. Trenkwalder, Henrik Wilming, Hans J. Briegel, Renato Renner

To make progress in science, we often build abstract representations of physical systems that meaningfully encode information about the systems.

Representation Learning

Quantum enhancements for deep reinforcement learning in large spaces

1 code implementation28 Oct 2019 Sofiene Jerbi, Lea M. Trenkwalder, Hendrik Poulsen Nautrup, Hans J. Briegel, Vedran Dunjko

In the past decade, the field of quantum machine learning has drawn significant attention due to the prospect of bringing genuine computational advantages to now widespread algorithmic methods.

Decision Making reinforcement-learning

On the convergence of projective-simulation-based reinforcement learning in Markov decision processes

no code implementations25 Oct 2019 Walter L. Boyajian, Jens Clausen, Lea M. Trenkwalder, Vedran Dunjko, Hans J. Briegel

Specifically, we prove that one version of the projective simulation model, understood as a reinforcement learning approach, converges to optimal behavior in a large class of Markov decision processes.

reinforcement-learning

How a minimal learning agent can infer the existence of unobserved variables in a complex environment

no code implementations15 Oct 2019 Katja Ried, Benjamin Eva, Thomas Müller, Hans J. Briegel

According to a mainstream position in contemporary cognitive science and philosophy, the use of abstract compositional concepts is both a necessary and a sufficient condition for the presence of genuine thought.

Explainable artificial intelligence

Photonic architecture for reinforcement learning

no code implementations17 Jul 2019 Fulvio Flamini, Arne Hamann, Sofiène Jerbi, Lea M. Trenkwalder, Hendrik Poulsen Nautrup, Hans J. Briegel

The last decade has seen an unprecedented growth in artificial intelligence and photonic technologies, both of which drive the limits of modern-day computing devices.

Active Learning Q-Learning +1

Machine learning for long-distance quantum communication

2 code implementations24 Apr 2019 Julius Wallnöfer, Alexey A. Melnikov, Wolfgang Dür, Hans J. Briegel

But can it also be used to find novel protocols and algorithms for applications such as large-scale quantum communication?

Decision Making

Optimizing Quantum Error Correction Codes with Reinforcement Learning

no code implementations20 Dec 2018 Hendrik Poulsen Nautrup, Nicolas Delfosse, Vedran Dunjko, Hans J. Briegel, Nicolai Friis

Using efficient simulations with about 70 data qubits with arbitrary connectivity, we demonstrate that such a reinforcement learning agent can determine near-optimal solutions, in terms of the number of data qubits, for various error models of interest.

reinforcement-learning Transfer Learning

Benchmarking projective simulation in navigation problems

no code implementations23 Apr 2018 Alexey A. Melnikov, Adi Makmal, Hans J. Briegel

Projective simulation (PS) is a model for intelligent agents with a deliberation capacity that is based on episodic memory.

Q-Learning reinforcement-learning

Modelling collective motion based on the principle of agency

no code implementations4 Dec 2017 Katja Ried, Thomas Müller, Hans J. Briegel

Collective motion is an intriguing phenomenon, especially considering that it arises from a set of simple rules governing local interactions between individuals.

Machine learning \& artificial intelligence in the quantum domain

no code implementations8 Sep 2017 Vedran Dunjko, Hans J. Briegel

For instance, quantum computing is finding a vital application in providing speed-ups in ML, critical in our "big data" world.

Speeding-up the decision making of a learning agent using an ion trap quantum processor

no code implementations5 Sep 2017 Theeraphot Sriarunothai, Sabine Wölk, Gouri Shankar Giri, Nicolai Friis, Vedran Dunjko, Hans J. Briegel, Christof Wunderlich

We report a proof-of-principle experimental demonstration of the quantum speed-up for learning agents utilizing a small-scale quantum information processor based on radiofrequency-driven trapped ions.

Decision Making

Active learning machine learns to create new quantum experiments

no code implementations2 Jun 2017 Alexey A. Melnikov, Hendrik Poulsen Nautrup, Mario Krenn, Vedran Dunjko, Markus Tiersch, Anton Zeilinger, Hans J. Briegel

We investigate this question by using the projective simulation model, a physics-oriented approach to artificial intelligence.

Active Learning

Quantum-enhanced machine learning

no code implementations26 Oct 2016 Vedran Dunjko, Jacob M. Taylor, Hans J. Briegel

Within our approach, we tackle the problem of quantum enhancements in reinforcement learning as well, and propose a systematic scheme for providing improvements.

reinforcement-learning

Meta-learning within Projective Simulation

no code implementations25 Feb 2016 Adi Makmal, Alexey A. Melnikov, Vedran Dunjko, Hans J. Briegel

The extended model is examined on three different kinds of reinforcement learning tasks, in which the agent has different optimal values of the meta-parameters, and is shown to perform well, reaching near-optimal to optimal success rates in all of them, without ever needing to manually adjust any meta-parameter.

Meta-Learning reinforcement-learning

Quantum machine learning with glow for episodic tasks and decision games

no code implementations27 Jan 2016 Jens Clausen, Hans J. Briegel

We consider a general class of models, where a reinforcement learning (RL) agent learns from cyclic interactions with an external environment via classical signals.

Framework for learning agents in quantum environments

no code implementations30 Jul 2015 Vedran Dunjko, Jacob M. Taylor, Hans J. Briegel

In this paper we provide a broad framework for describing learning agents in general quantum environments.

Projective simulation with generalization

no code implementations9 Apr 2015 Alexey A. Melnikov, Adi Makmal, Vedran Dunjko, Hans J. Briegel

Specifically, we show that already in basic (but extreme) environments, learning without generalization may be impossible, and demonstrate how the presented generalization machinery enables the projective simulation agent to learn.

Faster quantum mixing for slowly evolving sequences of Markov chains

no code implementations4 Mar 2015 Davide Orsucci, Hans J. Briegel, Vedran Dunjko

Markov chain methods are remarkably successful in computational physics, machine learning, and combinatorial optimization.

Combinatorial Optimization

Projective simulation applied to the grid-world and the mountain-car problem

no code implementations21 May 2014 Alexey A. Melnikov, Adi Makmal, Hans J. Briegel

We compare the performance of the PS agent model with those of existing models and show that the PS agent exhibits competitive performance also in such scenarios.

reinforcement-learning

Projective simulation for classical learning agents: a comprehensive investigation

no code implementations7 May 2013 Julian Mautner, Adi Makmal, Daniel Manzano, Markus Tiersch, Hans J. Briegel

We study the model of projective simulation (PS), a novel approach to artificial intelligence based on stochastic processing of episodic memory which was recently introduced [H. J.

Q-Learning reinforcement-learning

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