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1 code implementation • 15 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**

no code implementations • 20 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.

1 code implementation • 3 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.

no code implementations • 20 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.

1 code implementation • 10 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.

no code implementations • 31 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.

1 code implementation • 24 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.

1 code implementation • 25 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.

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.

no code implementations • 14 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.

no code implementations • 1 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.

2 code implementations • 2 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.

1 code implementation • 28 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.

no code implementations • 25 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.

no code implementations • 15 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**

no code implementations • 17 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.

2 code implementations • 24 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?

no code implementations • 20 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.

no code implementations • 23 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.

no code implementations • 4 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.

no code implementations • 8 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.

no code implementations • 5 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.

no code implementations • 2 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.

no code implementations • 26 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.

no code implementations • 25 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.

no code implementations • 27 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.

no code implementations • 30 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.

no code implementations • 9 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.

no code implementations • 4 Mar 2015 • Davide Orsucci, Hans J. Briegel, Vedran Dunjko

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

no code implementations • 21 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.

no code implementations • 7 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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