Research
Here you can find a complete list of papers using PS:
2026
Interpreting Quantum Learning Models via Stochastic Processes by J. Fankhauser, L. J. Fiderer and H. J. Briegel (2026).
Emergent aggregation from collective foraging by G. Muñoz-Gil, A. López-Incera, V. Ramsten, G. Volpe, T. Müller and H. J. Briegel (2026).
Multi-excitation projective simulation with a many-body physics-inspired inductive bias by P. A. LeMaitre, M. Krumm and H. J. Briegel (2026).
Memory Traces on an Episodic-Semantic Continuum: Learning and Recall in a Unifying Computational Model by A. Q. Vining, A. López-Incera, J. Pazem, H. J. Briegel and T. Müller (2026).
(*) Emergence of Longitudinal Queues in Group Navigation: An Interpretable Approach via Projective Simulation by D. Kong, K. Xue, P. Wang and Z. Xu (2026).
2025
Projective Simulation in Action (book, Synthese Library) by H. J. Briegel and T. Müller (2025).
Free Energy Projective Simulation (FEPS): Active inference with interpretability by J. Pazem, M. Krumm, A. Q. Vining, L. J. Fiderer and H. J. Briegel (2025).
Run-and-Tumble Particles Learning Chemotaxis by N. Tovazzi, G. Muñoz-Gil and M. Caraglio (2025).
Learning to reset in target search problems by G. Muñoz-Gil, H. J. Briegel and M. Caraglio (2025).
(*) Enhancing Quantum Key Distribution with Quantum Projective Simulation by M. Ghadimi, S. S. Nande, H. K. Nazari, R. Bassoli and F. H. P. Fitzek (2025).
(*) Elucidating simulated equivalence responding through dynamic visualization of structural connectivity and relational density by J. O’Sullivan, F. Jackson Brown and O. Ray (2025).
2024
Learning how to find targets in the micro-world: the case of intermittent active Brownian particles by M. Caraglio, H. Kaur, L. J. Fiderer, A. López-Incera, H. J. Briegel, T. Franosch and G. Muñoz-Gil (2024).
Optimal foraging strategies can be learned by G. Muñoz-Gil, A. López-Incera, L. J. Fiderer and H. J. Briegel (2024).
Towards interpretable quantum machine learning via single-photon quantum walks by F. Flamini, M. Krumm, L. J. Fiderer, T. Müller and H. J. Briegel (2024).
Hybrid discrete-continuous compilation of trapped-ion quantum circuits with deep reinforcement learning by F. Preti, M. Schilling, S. Jerbi, L. M. Trenkwalder, H. Poulsen Nautrup, F. Motzoi and H. J. Briegel (2024).
(*) Demonstration of quantum projective simulation on a single-photon-based quantum computer by G. Franceschetto and A. Ricou (2024).
(*) Entanglement-enhanced Quantum Reinforcement Learning: an Application using Single-Photons by J. M. Gaspar, A. Bergerault, V. Apostolou and A. Ricou (2024).
(*) Optimization of tensor network codes with reinforcement learning by C. Mauron, T. Farrelly and T. M. Stace (2024).
2023
- How a Minimal Learning Agent can Infer the Existence of Unobserved Variables in a Complex Environment by B. Eva, K. Ried, T. Müller and H. J. Briegel (2023).
2021
Honeybee communication during collective defence is shaped by predation by A. López-Incera, M. Nouvian, K. Ried, T. Müller and H. J. Briegel (2021).
Experimental quantum speed-up in reinforcement learning agents by V. Saggio, B. E. Asenbeck, A. Hamann, T. Strömberg, P. Schiansky, V. Dunjko, N. Friis, N. C. Harris, M. Hochberg, D. Englund, S. Wölk, H. J. Briegel and P. Walther (2021).
Quantum Enhancements for Deep Reinforcement Learning in Large Spaces by S. Jerbi, L. M. Trenkwalder, H. Poulsen Nautrup, H. J. Briegel and V. Dunjko (2021).
(*) Quantum Circuit Synthesis Using Projective Simulation by O. Menegasso Pires, E. Inacio Duzzioni, J. Marchi and R. De Santiago (2021).
(*) A projective simulation scheme for partially observable multi-agent systems by R. Kheiri (2021).
(*) Enhanced Equivalence Projective Simulation: A Framework for Modeling Formation of Stimulus Equivalence Classes by A. Abolpour Mofrad, A. Yazidi, S. Abolpour Mofrad, H. L. Hammer and E. Arntzen (2021).
2020
Machine Learning for Long-Distance Quantum Communication by J. Wallnöfer, A. A. Melnikov, W. Dür and H. J. Briegel (2020).
Photonic architecture for reinforcement learning by F. Flamini, A. Hamann, S. Jerbi, L. M. Trenkwalder, H. Poulsen Nautrup and H. J. Briegel (2020).
Skill Learning by Autonomous Robotic Playing Using Active Learning and Exploratory Behavior Composition by S. Hangl, V. Dunjko, H. J. Briegel and J. Piater (2020).
On the convergence of projective-simulation-based reinforcement learning in Markov decision processes by W. L. Boyajian, J. Clausen, L. M. Trenkwalder, V. Dunjko and H. J. Briegel (2020).
Development of swarm behavior in artificial learning agents that adapt to different foraging environments by A. López-Incera, K. Ried, T. Müller and H. J. Briegel (2020).
(*) Equivalence Projective Simulation as a Framework for Modeling Formation of Stimulus Equivalence Classes by A. Abolpour Mofrad, A. Yazidi, H. L. Hammer and E. Arntzen (2020).
2019
Modelling collective motion based on the principle of agency: General framework and the case of marching locusts by K. Ried, T. Müller and H. J. Briegel (2019).
Optimizing Quantum Error Correction Codes with Reinforcement Learning by H. Poulsen Nautrup, N. Delfosse, V. Dunjko, H. J. Briegel and N. Friis (2019).
Speeding-up the decision making of a learning agent using an ion trap quantum processor by T. Sriarunothai, S. Wölk, G. S. Giri, N. Friis, V. Dunjko, H. J. Briegel and C. Wunderlich (2019).
2018
Active learning machine learns to create new quantum experiments by A. A. Melnikov, H. Poulsen Nautrup, M. Krenn, V. Dunjko, M. Tiersch, A. Zeilinger and H. J. Briegel (2018).
Benchmarking Projective Simulation in Navigation Problems by A. A. Melnikov, A. Makmal and H. J. Briegel (2018).
Quantum machine learning with glow for episodic tasks and decision games by J. Clausen and H. J. Briegel (2018).
A Stochastic Process Model for Free Agency under Indeterminism by T. Müller (2018).
2017
Projective simulation with generalization by A. A. Melnikov, A. Makmal, V. Dunjko and H. J. Briegel (2017).
(*) Quantum Projective Simulation with Hamiltonian Evolution: A study in reinforcement learning by A. Katabarwa and N. Karimatari (2017).
(*) A novel skill-based programming paradigm based on autonomous playing and skill-centric testing by S. Hangl, A. Mennel and J. Piater (2017).
(*) Autonomous robots: potential, advances and future direction by S. Hangl, E. Ugur and J. Piater (2017).
2016
Meta-learning within Projective Simulation by A. Makmal, A. A. Melnikov, V. Dunjko and H. J. Briegel (2016).
(*) Robotic playing for hierarchical complex skill learning by S. Hangl, E. Ugur, S. Szedmak and J. Piater (2016).
2015
Projective Simulation for Classical Learning Agents: A Comprehensive Investigation by J. Mautner, A. Makmal, D. Manzano, M. Tiersch and H. J. Briegel (2015).
Adaptive quantum computation in changing environments using projective simulation by M. Tiersch, E. J. Ganahl and H. J. Briegel (2015).
Quantum-enhanced deliberation of learning agents using trapped ions by V. Dunjko, N. Friis and H. J. Briegel (2015).
A Chance for Attributable Agency by H. J. Briegel and T. Müller (2015).
2014
Quantum Speedup for Active Learning Agents by G. D. Paparo, V. Dunjko, A. Makmal, M. A. Martin-Delgado and H. J. Briegel (2014).
Projective simulation applied to the grid-world and the mountain-car problem by A. A. Melnikov, A. Makmal and H. J. Briegel (2014).
2012
On creative machines and the physical origins of freedom by H. J. Briegel (2012).
Projective simulation for artificial intelligence by H. J. Briegel and G. De las Cuevas (2012).
(*) Papers by authors outside the Quantum Information and Computation group at the UIBK.