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ORCIDHans J. Briegel
Person information
- affiliation: University of Innsbruck, Institute for Theoretical Physics, Austria
- affiliation (former): Ludwig Maximilian University of Munich, Institute for Theoretical Physics, Germany
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Journal Articles
- 2024
[j15]Florian Fürrutter
, Gorka Muñoz-Gil, Hans J. Briegel:
Quantum circuit synthesis with diffusion models. Nat. Mac. Intell. 6(5): 515-524 (2024)- 2023
- [j14]Benjamin Eva, Katja Ried, Thomas Müller, Hans J. Briegel:
How a Minimal Learning Agent can Infer the Existence of Unobserved Variables in a Complex Environment. Minds Mach. 33(1): 185-219 (2023) - [j13]Lea M. Trenkwalder, Andrea López-Incera, Hendrik Poulsen Nautrup, Fulvio Flamini, Hans J. Briegel:
Automated gadget discovery in the quantum domain. Mach. Learn. Sci. Technol. 4(3): 35043 (2023) - [j12]Isaac D. Smith, Marius Krumm, Lukas J. Fiderer, Hendrik Poulsen Nautrup, Hans J. Briegel:
The Min-Entropy of Classical-Quantum Combs for Measurement-Based Applications. Quantum 7: 1206 (2023) - 2022
- [j11]Hendrik Poulsen Nautrup, Tony Metger, Raban Iten, Sofiène Jerbi, Lea M. Trenkwalder, Henrik Wilming, Hans J. Briegel, Renato Renner:
Operationally meaningful representations of physical systems in neural networks. Mach. Learn. Sci. Technol. 3(4): 45025 (2022) - 2021
- [j10]Valeria Saggio, Beate E. Asenbeck, Arne Hamann, Teodor Strömberg, Peter Schiansky, Vedran Dunjko, Nicolai Friis, Nicholas C. Harris, Michael Hochberg, Dirk R. Englund, Sabine Wölk, Hans J. Briegel, Philip Walther:
Experimental quantum speed-up in reinforcement learning agents. Nat. 591(7849): 229-233 (2021) - 2020
- [j9]Simon Hangl, Vedran Dunjko, Hans J. Briegel, Justus H. Piater:
Skill Learning by Autonomous Robotic Playing Using Active Learning and Exploratory Behavior Composition. Frontiers Robotics AI 7: 42 (2020) - [j8]Walter L. Boyajian, Jens Clausen, Lea M. Trenkwalder, Vedran Dunjko, Hans J. Briegel:
On the convergence of projective-simulation-based reinforcement learning in Markov decision processes. Quantum Mach. Intell. 2(2): 1-21 (2020) - 2018
- [j7]Alexey A. Melnikov, Adi Makmal, Hans J. Briegel:
Benchmarking Projective Simulation in Navigation Problems. IEEE Access 6: 64639-64648 (2018) - 2016
- [j6]Adi Makmal, Alexey A. Melnikov, Vedran Dunjko, Hans J. Briegel:
Meta-learning within Projective Simulation. IEEE Access 4: 2110-2122 (2016) - 2015
- [j5]Hans J. Briegel, Thomas Müller:
A Chance for Attributable Agency. Minds Mach. 25(3): 261-279 (2015) - [j4]Julian Mautner, Adi Makmal, Daniel Manzano, Markus Tiersch, Hans J. Briegel:
Projective Simulation for Classical Learning Agents: A Comprehensive Investigation. New Gener. Comput. 33(1): 69-114 (2015) - 2014
- [j3]Alexey A. Melnikov, Adi Makmal, Hans J. Briegel:
Projective simulation applied to the grid-world and the mountain-car problem. Artif. Intell. Res. 3(3): 24-34 (2014) - 2004
- [j2]Hans Aschauer, John Calsamiglia, Marc Hein, Hans J. Briegel:
Local invariants for multi-partite entangled states allowing for a simple entanglement criterion. Quantum Inf. Comput. 4(5): 383-395 (2004) - 2002
- [j1]Robert Raussendorf, Hans J. Briegel:
Computational model underlying the one-way quantum computer. Quantum Inf. Comput. 2(6): 443-486 (2002)
Conference and Workshop Papers
- 2021
- [c4]Sofiène Jerbi, Casper Gyurik, Simon C. Marshall, Hans J. Briegel, Vedran Dunjko:
Parametrized Quantum Policies for Reinforcement Learning. NeurIPS 2021: 28362-28375 - 2017
- [c3]Vedran Dunjko, Jacob M. Taylor, Hans J. Briegel:
Advances in quantum reinforcement learning. SMC 2017: 282-287 - 2016
- [c2]Jacob M. Taylor, Hans J. Briegel, Vedran Dunjko:
Enhanced learning for agents in quantum-accessible environments. ESANN 2016 - 1998
- [c1]Hans J. Briegel, J. Ignacio Cirac, Wolfgang Dür, Steven J. van Enk, H. Jeff Kimble, Hideo Mabuchi, Peter Zoller:
Physical Implementations for Quantum Communication in Quantum Networks. QCQC 1998: 373-382
Reference Works
- 2009
- [r1]Hans J. Briegel:
Cluster States. Compendium of Quantum Physics 2009: 96-105
Informal and Other Publications
- 2024
- [i32]Philip A. LeMaitre, Marius Krumm, Hans J. Briegel:
Multi-Excitation Projective Simulation with a Many-Body Physics Inspired Inductive Bias. CoRR abs/2402.10192 (2024) - 2023
- [i31]Fulvio Flamini, Marius Krumm, Lukas J. Fiderer, Thomas Müller, Hans J. Briegel:
Reinforcement learning and decision making via single-photon quantum walks. CoRR abs/2301.13669 (2023) - [i30]Gorka Muñoz-Gil, Andrea López-Incera, Lukas J. Fiderer, Hans J. Briegel:
Optimal foraging strategies can be learned and outperform Lévy walks. CoRR abs/2303.06050 (2023) - [i29]Arunava Majumder, Marius Krumm, Tina Radkohl, Hendrik Poulsen Nautrup, Sofiène Jerbi, Hans J. Briegel:
Variational measurement-based quantum computation for generative modeling. CoRR abs/2310.13524 (2023) - [i28]Florian Fürrutter, Gorka Muñoz-Gil, Hans J. Briegel:
Quantum circuit synthesis with diffusion models. CoRR abs/2311.02041 (2023) - [i27]Hendrik Poulsen Nautrup, Hans J. Briegel:
Measurement-based quantum computation from Clifford quantum cellular automata. CoRR abs/2312.13185 (2023) - 2022
- [i26]Lea M. Trenkwalder, Andrea López-Incera, Hendrik Poulsen Nautrup, Fulvio Flamini, Hans J. Briegel:
Automated Gadget Discovery in Science. CoRR abs/2212.12743 (2022) - 2021
- [i25]Sofiène Jerbi, Casper Gyurik, Simon C. Marshall, Hans J. Briegel, Vedran Dunjko:
Variational quantum policies for reinforcement learning. CoRR abs/2103.05577 (2021) - [i24]Sofiène Jerbi, Lukas J. Fiderer, Hendrik Poulsen Nautrup, Jonas M. Kübler, Hans J. Briegel, Vedran Dunjko:
Quantum machine learning beyond kernel methods. CoRR abs/2110.13162 (2021) - 2020
- [i23]Hendrik Poulsen Nautrup, Tony Metger, Raban Iten, Sofiène Jerbi, Lea M. Trenkwalder, Henrik Wilming, Hans J. Briegel, Renato Renner:
Operationally meaningful representations of physical systems in neural networks. CoRR abs/2001.00593 (2020) - [i22]Andrea López-Incera, Katja Ried, Thomas Müller, Hans J. Briegel:
Development of swarm behavior in artificial learning agents that adapt to different foraging environments. CoRR abs/2004.00552 (2020) - [i21]Andrea López-Incera, Morgane Nouvian, Katja Ried, Thomas Müller, Hans J. Briegel:
Collective defense of honeybee colonies: experimental results and theoretical modeling. CoRR abs/2010.07326 (2020) - 2019
- [i20]Julius Wallnöfer, Alexey A. Melnikov, Wolfgang Dür, Hans J. Briegel:
Machine learning for long-distance quantum communication. CoRR abs/1904.10797 (2019) - [i19]Fulvio Flamini, Arne Hamann, Sofiène Jerbi, Lea M. Trenkwalder, Hendrik Poulsen Nautrup, Hans J. Briegel:
Photonic architecture for reinforcement learning. CoRR abs/1907.07503 (2019) - [i18]Katja Ried, Benjamin Eva, Thomas Müller, Hans J. Briegel:
How a minimal learning agent can infer the existence of unobserved variables in a complex environment. CoRR abs/1910.06985 (2019) - [i17]Jens Clausen, Walter L. Boyajian, Lea M. Trenkwalder, Vedran Dunjko, Hans J. Briegel:
On the convergence of projective-simulation-based reinforcement learning in Markov decision processes. CoRR abs/1910.11914 (2019) - [i16]Sofiène Jerbi, Hendrik Poulsen Nautrup, Lea M. Trenkwalder, Hans J. Briegel, Vedran Dunjko:
A framework for deep energy-based reinforcement learning with quantum speed-up. CoRR abs/1910.12760 (2019) - 2018
- [i15]Alexey A. Melnikov, Adi Makmal, Hans J. Briegel:
Benchmarking projective simulation in navigation problems. CoRR abs/1804.08607 (2018) - [i14]Hendrik Poulsen Nautrup, Nicolas Delfosse, Vedran Dunjko, Hans J. Briegel, Nicolai Friis:
Optimizing Quantum Error Correction Codes with Reinforcement Learning. CoRR abs/1812.08451 (2018) - 2017
- [i13]Alexey A. Melnikov, Hendrik Poulsen Nautrup, Mario Krenn, Vedran Dunjko, Markus Tiersch, Anton Zeilinger, Hans J. Briegel:
Active learning machine learns to create new quantum experiments. CoRR abs/1706.00868 (2017) - [i12]Simon Hangl, Vedran Dunjko, Hans J. Briegel, Justus H. Piater:
Skill Learning by Autonomous Robotic Playing using Active Learning and Creativity. CoRR abs/1706.08560 (2017) - [i11]Theeraphot Sriarunothai, Sabine Wölk, Gouri Shankar Giri, Nicolai Friis, Vedran Dunjko, Hans J. Briegel, Christof Wunderlich:
Speeding-up the decision making of a learning agent using an ion trap quantum processor. CoRR abs/1709.01366 (2017) - [i10]Vedran Dunjko, Hans J. Briegel:
Machine learning \& artificial intelligence in the quantum domain. CoRR abs/1709.02779 (2017) - 2016
- [i9]Jens Clausen, Hans J. Briegel:
Quantum machine learning with glow for episodic tasks and decision games. CoRR abs/1601.07358 (2016) - [i8]Adi Makmal, Alexey A. Melnikov, Vedran Dunjko, Hans J. Briegel:
Meta-learning within Projective Simulation. CoRR abs/1602.08017 (2016) - [i7]Vedran Dunjko, Jacob M. Taylor, Hans J. Briegel:
Quantum-enhanced machine learning. CoRR abs/1610.08251 (2016) - 2015
- [i6]Vedran Dunjko, Hans J. Briegel:
Quantum mixing of Markov chains for special distributions. CoRR abs/1502.05511 (2015) - [i5]Vedran Dunjko, Hans J. Briegel:
Sequential quantum mixing for slowly evolving sequences of Markov chains. CoRR abs/1503.01334 (2015) - [i4]Alexey A. Melnikov, Adi Makmal, Vedran Dunjko, Hans J. Briegel:
Projective simulation with generalization. CoRR abs/1504.02247 (2015) - [i3]Vedran Dunjko, Jacob M. Taylor, Hans J. Briegel:
Framework for learning agents in quantum environments. CoRR abs/1507.08482 (2015) - 2014
- [i2]Alexey A. Melnikov, Adi Makmal, Hans J. Briegel:
Projective simulation applied to the grid-world and the mountain-car problem. CoRR abs/1405.5459 (2014) - 2013
- [i1]Julian Mautner, Adi Makmal, Daniel Manzano, Markus Tiersch, Hans J. Briegel:
Projective simulation for classical learning agents: a comprehensive investigation. CoRR abs/1305.1578 (2013)
Coauthor Index
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