Search Results for author: Hans J. Briegel

Found 31 papers, 8 papers with code

Multi-Excitation Projective Simulation with a Many-Body Physics Inspired Inductive Bias

1 code implementation15 Feb 2024 Philip A. LeMaitre, Marius Krumm, Hans J. Briegel

To overcome this limitation, we introduce Multi-Excitation Projective Simulation (mePS), a generalization that considers a chain-of-thought to be a random walk of several particles on a hypergraph.

Explainable artificial intelligence Explainable Artificial Intelligence (XAI) +2

Measurement-based quantum computation from Clifford quantum cellular automata

no code implementations20 Dec 2023 Hendrik Poulsen Nautrup, Hans J. Briegel

Measurement-based quantum computation (MBQC) is a paradigm for quantum computation where computation is driven by local measurements on a suitably entangled resource state.

Quantum circuit synthesis with diffusion models

1 code implementation3 Nov 2023 Florian Fürrutter, Gorka Muñoz-Gil, Hans J. Briegel

The model excels at generating new circuits and supports typical DM extensions such as masking and editing to, for instance, align the circuit generation to the constraints of the targeted quantum device.

Denoising Quantum Circuit Generation

Variational measurement-based quantum computation for generative modeling

no code implementations20 Oct 2023 Arunava Majumder, Marius Krumm, Tina Radkohl, Hendrik Poulsen Nautrup, Sofiene Jerbi, Hans J. Briegel

Measurement-based quantum computation (MBQC) offers a fundamentally unique paradigm to design quantum algorithms.

Optimal foraging strategies can be learned

1 code implementation10 Mar 2023 Gorka Muñoz-Gil, Andrea López-Incera, Lukas J. Fiderer, Hans J. Briegel

Recognizing the interconnected nature of these challenges, this work addresses them simultaneously by exploring optimal foraging strategies through a reinforcement learning framework.

reinforcement-learning Reinforcement Learning (RL)

Towards interpretable quantum machine learning via single-photon quantum walks

no code implementations31 Jan 2023 Fulvio Flamini, Marius Krumm, Lukas J. Fiderer, Thomas Müller, Hans J. Briegel

Variational quantum algorithms represent a promising approach to quantum machine learning where classical neural networks are replaced by parametrized quantum circuits.

Decision Making Quantum Machine Learning +3

Automated Gadget Discovery in Science

1 code implementation24 Dec 2022 Lea M. Trenkwalder, Andrea López Incera, Hendrik Poulsen Nautrup, Fulvio Flamini, Hans J. Briegel

This process of gadget discovery develops in three stages: First, we use an RL agent to generate data, then, we employ a mining algorithm to extract gadgets and finally, the obtained gadgets are grouped by a density-based clustering algorithm.

Clustering Reinforcement Learning (RL)

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

In this work, we identify a constructive framework that captures all standard models based on parametrized quantum circuits: that of linear quantum models.

BIG-bench Machine Learning Quantum Machine Learning

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.

Benchmarking reinforcement-learning +1

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.

BIG-bench Machine Learning Decision Making +3

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 Reinforcement Learning (RL)

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 Explainable Artificial Intelligence (XAI) +1

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 +2

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?

BIG-bench Machine Learning 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 Reinforcement Learning (RL) +1

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.

Benchmarking Q-Learning +2

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.

BIG-bench Machine Learning

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.

BIG-bench Machine Learning 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.

BIG-bench Machine Learning Quantum Machine Learning +2

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 +1

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.

BIG-bench Machine Learning Quantum Machine Learning +1

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.

BIG-bench Machine Learning 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.

Benchmarking reinforcement-learning +1

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.


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