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Vedran Dunjko
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- affiliation: Leiden University, The Netherlands
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2020 – today
- 2024
- [j20]Charles Moussa, Yash J. Patel, Vedran Dunjko, Thomas Bäck, Jan N. van Rijn:
Hyperparameter importance and optimization of quantum neural networks across small datasets. Mach. Learn. 113(4): 1941-1966 (2024) - [j19]Elies Gil-Fuster, Jens Eisert, Vedran Dunjko:
On the expressivity of embedding quantum kernels. Mach. Learn. Sci. Technol. 5(2): 25003 (2024) - [c14]Yash J. Patel, Akash Kundu, Mateusz Ostaszewski, Xavier Bonet-Monroig, Vedran Dunjko, Onur Danaci:
Curriculum reinforcement learning for quantum architecture search under hardware errors. ICLR 2024 - [c13]Marie C. Kempkes, Vedran Dunjko, Evert P. L. van Nieuwenburg, Jakob Spiegelberg:
Reliable Classifications with Guaranteed Confidence Using the Dempster-Shafer Theory of Evidence. ECML/PKDD (2) 2024: 89-105 - [i41]Yash J. Patel, Akash Kundu, Mateusz Ostaszewski, Xavier Bonet-Monroig, Vedran Dunjko, Onur Danaci:
Curriculum reinforcement learning for quantum architecture search under hardware errors. CoRR abs/2402.03500 (2024) - [i40]Simon C. Marshall, Casper Gyurik, Vedran Dunjko:
On Bounded Advice Classes. CoRR abs/2405.18155 (2024) - [i39]Elies Gil-Fuster, Casper Gyurik, Adrián Pérez-Salinas, Vedran Dunjko:
On the relation between trainability and dequantization of variational quantum learning models. CoRR abs/2406.07072 (2024) - 2023
- [j18]Casper Gyurik, Dyon van Vreumingen, Vedran Dunjko:
Structural risk minimization for quantum linear classifiers. Quantum 7: 893 (2023) - [j17]Mathys Rennela, Sebastiaan Brand, Alfons Laarman, Vedran Dunjko:
Hybrid divide-and-conquer approach for tree search algorithms. Quantum 7: 959 (2023) - [j16]Simon C. Marshall, Casper Gyurik, Vedran Dunjko:
High Dimensional Quantum Machine Learning With Small Quantum Computers. Quantum 7: 1078 (2023) - [j15]Lieuwe Vinkhuijzen, Tim Coopmans, David Elkouss, Vedran Dunjko, Alfons Laarman:
LIMDD: A Decision Diagram for Simulation of Quantum Computing Including Stabilizer States. Quantum 7: 1108 (2023) - [c12]Alice Barthe, Michele Grossi, Jordi Tura, Vedran Dunjko:
Continuous Variables Quantum Algorithm for Solving Ordinary Differential Equations. QCE 2023: 48-53 - [c11]Waheeda Saib, Xavier Bonet-Monroig, Vedran Dunjko, Ivano Tavernelli, Thomas Bäck, Hao Wang:
Benchmarking Adaptive Quantum Circuit Optimization Algorithms for Quantum Chemistry. QCE 2023: 83-88 - [c10]Charles Moussa, Hao Wang, Mauricio Araya-Polo, Thomas Bäck, Vedran Dunjko:
Application of quantum-inspired generative models to small molecular datasets. QCE 2023: 342-348 - [c9]Sofiène Jerbi, Arjan Cornelissen, Maris Ozols, Vedran Dunjko:
Quantum Policy Gradient Algorithms. TQC 2023: 13:1-13:24 - [i38]Charles Moussa, Hao Wang, Mauricio Araya-Polo, Thomas Bäck, Vedran Dunjko:
Application of quantum-inspired generative models to small molecular datasets. CoRR abs/2304.10867 (2023) - [i37]Sofiène Jerbi, Casper Gyurik, Simon C. Marshall, Riccardo Molteni, Vedran Dunjko:
Shadows of quantum machine learning. CoRR abs/2306.00061 (2023) - [i36]Akash Kundu, Przemyslaw Bedelek, Mateusz Ostaszewski, Onur Danaci, Yash J. Patel, Vedran Dunjko, Jaroslaw Adam Miszczak:
Enhancing variational quantum state diagonalization using reinforcement learning techniques. CoRR abs/2306.11086 (2023) - [i35]Casper Gyurik, Vedran Dunjko:
Exponential separations between classical and quantum learners. CoRR abs/2306.16028 (2023) - [i34]Elies Gil-Fuster, Jens Eisert, Vedran Dunjko:
On the expressivity of embedding quantum kernels. CoRR abs/2309.14419 (2023) - [i33]Lea M. Trenkwalder, Eleanor Scerri, Thomas E. O'Brien, Vedran Dunjko:
Compilation of product-formula Hamiltonian simulation via reinforcement learning. CoRR abs/2311.04285 (2023) - 2022
- [j14]Andrea Skolik, Sofiène Jerbi, Vedran Dunjko:
Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning. Quantum 6: 720 (2022) - [j13]Casper Gyurik, Chris Cade, Vedran Dunjko:
Towards quantum advantage via topological data analysis. Quantum 6: 855 (2022) - [c8]Charles Moussa, Jan N. van Rijn, Thomas Bäck, Vedran Dunjko:
Hyperparameter Importance of Quantum Neural Networks Across Small Datasets. DS 2022: 32-46 - [i32]Simon C. Marshall, Casper Gyurik, Vedran Dunjko:
High Dimensional Quantum Learning With Small Quantum Computers. CoRR abs/2203.13739 (2022) - [i31]Andrea Skolik, Michele Cattelan, Sheir Yarkoni, Thomas Bäck, Vedran Dunjko:
Equivariant quantum circuits for learning on weighted graphs. CoRR abs/2205.06109 (2022) - [i30]Charles Moussa, Jan N. van Rijn, Thomas Bäck, Vedran Dunjko:
Hyperparameter Importance of Quantum Neural Networks Across Small Datasets. CoRR abs/2206.09992 (2022) - [i29]Yash J. Patel, Sofiène Jerbi, Thomas Bäck, Vedran Dunjko:
Reinforcement Learning Assisted Recursive QAOA. CoRR abs/2207.06294 (2022) - [i28]Casper Gyurik, Vedran Dunjko:
On establishing learning separations between classical and quantum machine learning with classical data. CoRR abs/2208.06339 (2022) - [i27]Sofiène Jerbi, Arjan Cornelissen, Maris Ozols, Vedran Dunjko:
Quantum policy gradient algorithms. CoRR abs/2212.09328 (2022) - 2021
- [j12]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) - [j11]Zhikuan Zhao, Jack K. Fitzsimons, Patrick Rebentrost, Vedran Dunjko, Joseph F. Fitzsimons:
Smooth input preparation for quantum and quantum-inspired machine learning. Quantum Mach. Intell. 3(1): 1-6 (2021) - [j10]Arne Hamann, Vedran Dunjko, Sabine Wölk:
Quantum-accessible reinforcement learning beyond strictly epochal environments. Quantum Mach. Intell. 3(2): 1-18 (2021) - [j9]Saad Yalouz, Bruno Senjean, Filippo Miatto, Vedran Dunjko:
Encoding strongly-correlated many-boson wavefunctions on a photonic quantum computer: application to the attractive Bose-Hubbard model. Quantum 5: 572 (2021) - [j8]Davide Orsucci, Vedran Dunjko:
On solving classes of positive-definite quantum linear systems with quadratically improved runtime in the condition number. Quantum 5: 573 (2021) - [c7]Charles Moussa, Hao Wang, Henri Calandra, Thomas Bäck, Vedran Dunjko:
Tabu-Driven Quantum Neighborhood Samplers. EvoCOP 2021: 100-119 - [c6]Mateusz Ostaszewski, Lea M. Trenkwalder, Wojciech Masarczyk, Eleanor Scerri, Vedran Dunjko:
Reinforcement learning for optimization of variational quantum circuit architectures. NeurIPS 2021: 18182-18194 - [c5]Sofiène Jerbi, Casper Gyurik, Simon C. Marshall, Hans J. Briegel, Vedran Dunjko:
Parametrized Quantum Policies for Reinforcement Learning. NeurIPS 2021: 28362-28375 - [i26]Sofiène Jerbi, Casper Gyurik, Simon C. Marshall, Hans J. Briegel, Vedran Dunjko:
Variational quantum policies for reinforcement learning. CoRR abs/2103.05577 (2021) - [i25]Mateusz Ostaszewski, Lea M. Trenkwalder, Wojciech Masarczyk, Eleanor Scerri, Vedran Dunjko:
Reinforcement learning for optimization of variational quantum circuit architectures. CoRR abs/2103.16089 (2021) - [i24]Casper Gyurik, Dyon van Vreumingen, Vedran Dunjko:
Structural risk minimization for quantum linear classifiers. CoRR abs/2105.05566 (2021) - [i23]Lieuwe Vinkhuijzen, Tim Coopmans, David Elkouss, Vedran Dunjko, Alfons Laarman:
LIMDD A Decision Diagram for Simulation of Quantum Computing Including Stabilizer States. CoRR abs/2108.00931 (2021) - [i22]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
- [j7]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) - [j6]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) - [i21]Casper Gyurik, Chris Cade, Vedran Dunjko:
Towards quantum advantage for topological data analysis. CoRR abs/2005.02607 (2020) - [i20]Mathys Rennela, Alfons Laarman, Vedran Dunjko:
Hybrid divide-and-conquer approach for tree search algorithms. CoRR abs/2007.07040 (2020)
2010 – 2019
- 2019
- [i19]Yimin Ge, Vedran Dunjko:
A hybrid algorithm framework for small quantum computers with application to finding Hamiltonian cycles. CoRR abs/1907.01258 (2019) - [i18]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) - [i17]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
- [i16]Vedran Dunjko, Yimin Ge, J. Ignacio Cirac:
Computational speedups using small quantum devices. CoRR abs/1807.08970 (2018) - [i15]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
- [c4]Vedran Dunjko, Jacob M. Taylor, Hans J. Briegel:
Advances in quantum reinforcement learning. SMC 2017: 282-287 - [i14]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) - [i13]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) - [i12]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) - [i11]Vedran Dunjko, Hans J. Briegel:
Machine learning \& artificial intelligence in the quantum domain. CoRR abs/1709.02779 (2017) - [i10]Vedran Dunjko, Yi-Kai Liu, Xingyao Wu, Jacob M. Taylor:
Super-polynomial separations for quantum-enhanced reinforcement learning. CoRR abs/1710.11160 (2017) - 2016
- [j5]Adi Makmal, Alexey A. Melnikov, Vedran Dunjko, Hans J. Briegel:
Meta-learning within Projective Simulation. IEEE Access 4: 2110-2122 (2016) - [j4]Vedran Dunjko, Theodoros Kapourniotis, Elham Kashefi:
Quantum-enhanced secure delegated classical computing. Quantum Inf. Comput. 16(1&2): 61-86 (2016) - [c3]Jacob M. Taylor, Hans J. Briegel, Vedran Dunjko:
Enhanced learning for agents in quantum-accessible environments. ESANN 2016 - [i9]Adi Makmal, Alexey A. Melnikov, Vedran Dunjko, Hans J. Briegel:
Meta-learning within Projective Simulation. CoRR abs/1602.08017 (2016) - [i8]Vedran Dunjko, Elham Kashefi:
Blind quantum computing with two almost identical states. CoRR abs/1604.01586 (2016) - [i7]Vedran Dunjko, Jacob M. Taylor, Hans J. Briegel:
Quantum-enhanced machine learning. CoRR abs/1610.08251 (2016) - 2015
- [j3]Tomoyuki Morimae, Vedran Dunjko, Elham Kashefi:
Ground state blind quantum computation on AKLT state. Quantum Inf. Comput. 15(3&4): 200-234 (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
- [c2]Vedran Dunjko, Joseph F. Fitzsimons, Christopher Portmann, Renato Renner:
Composable Security of Delegated Quantum Computation. ASIACRYPT (2) 2014: 406-425 - 2013
- [j2]Vedran Dunjko, Elham Kashefi:
Extended phase map decompositions for unitaries. Math. Struct. Comput. Sci. 23(2): 360-385 (2013) - [i2]Vedran Dunjko, Joseph F. Fitzsimons, Christopher Portmann, Renato Renner:
Composable security of delegated quantum computation. CoRR abs/1301.3662 (2013) - 2012
- [j1]Sanja Singer, Sasa Singer, Vedran Novakovic, Aleksandar Uscumlic, Vedran Dunjko:
Novel modifications of parallel Jacobi algorithms. Numer. Algorithms 59(1): 1-27 (2012) - 2010
- [c1]Vedran Dunjko, Elham Kashefi:
Algebraic characterisation of one-way patterns. DCM 2010: 85-100 - [i1]Sanja Singer, Sasa Singer, Vedran Novakovic, Aleksandar Uscumlic, Vedran Dunjko:
Novel Modifications of Parallel Jacobi Algorithms. CoRR abs/1008.0201 (2010)
Coauthor Index
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