Algebraic characterisation of one-way patterns - Vedran Dunjko; Elham Kashefi (DCM, 2010)

Novel modifications of parallel Jacobi algorithms. - Sanja Singer; Sasa Singer; Vedran Novakovic; Aleksandar Uscumlic; Vedran Dunjko (Numer. Algorithms, 2012)

Extended phase map decompositions for unitaries. - Vedran Dunjko; Elham Kashefi (Math. Struct. Comput. Sci., 2013)

Composable Security of Delegated Quantum Computation. - Vedran Dunjko; Joseph F. Fitzsimons; Christopher Portmann; Renato Renner (ASIACRYPT (2), 2014)

Quantum mixing of Markov chains for special distributions. - Vedran Dunjko; Hans J. Briegel (CoRR, 2015)

Sequential quantum mixing for slowly evolving sequences of Markov chains. - Vedran Dunjko; Hans J. Briegel (CoRR, 2015)

Framework for learning agents in quantum environments. - Vedran Dunjko; Jacob M. Taylor; Hans J. Briegel (CoRR, 2015)

Projective simulation with generalization. - Alexey A. Melnikov 001; Adi Makmal; Vedran Dunjko; Hans J. Briegel (CoRR, 2015)

Ground state blind quantum computation on AKLT state. - Tomoyuki Morimae; Vedran Dunjko; Elham Kashefi (Quantum Inf. Comput., 2015)

Enhanced learning for agents in quantum-accessible environments. - Jacob M. Taylor; Hans J. Briegel; Vedran Dunjko (ESANN, 2016)

Meta-learning within Projective Simulation. - Adi Makmal; Alexey A. Melnikov 001; Vedran Dunjko; Hans J. Briegel (IEEE Access, 2016)

Blind quantum computing with two almost identical states. - Vedran Dunjko; Elham Kashefi (CoRR, 2016)

Quantum-enhanced machine learning. - Vedran Dunjko; Jacob M. Taylor; Hans J. Briegel (CoRR, 2016)

Quantum-enhanced secure delegated classical computing. - Vedran Dunjko; Theodoros Kapourniotis; Elham Kashefi (Quantum Inf. Comput., 2016)

Advances in quantum reinforcement learning. - Vedran Dunjko; Jacob M. Taylor; Hans J. Briegel (SMC, 2017)

Skill Learning by Autonomous Robotic Playing using Active Learning and Creativity. - Simon Hangl; Vedran Dunjko; Hans J. Briegel; Justus H. Piater (CoRR, 2017)

Active learning machine learns to create new quantum experiments. - Alexey A. Melnikov 001; Hendrik Poulsen Nautrup; Mario Krenn; Vedran Dunjko; Markus Tiersch; Anton Zeilinger; Hans J. Briegel (CoRR, 2017)

Speeding-up the decision making of a learning agent using an ion trap quantum processor. - Theeraphot Sriarunothai; Sabine Wölk; Gouri Shankar Giri; Nicolai Friis; Vedran Dunjko; Hans J. Briegel; Christof Wunderlich (CoRR, 2017)

Machine learning & artificial intelligence in the quantum domain. - Vedran Dunjko; Hans J. Briegel (CoRR, 2017)

Super-polynomial separations for quantum-enhanced reinforcement learning. - Vedran Dunjko; Yi-Kai Liu 001; Xingyao Wu; Jacob M. Taylor (CoRR, 2017)

Computational speedups using small quantum devices. - Vedran Dunjko; Yimin Ge; J. Ignacio Cirac (CoRR, 2018)

Faster quantum mixing for slowly evolving sequences of Markov chains. - Davide Orsucci; Hans J. Briegel; Vedran Dunjko (Quantum, 2018)

A hybrid algorithm framework for small quantum computers with application to finding Hamiltonian cycles. - Yimin Ge; Vedran Dunjko (CoRR, 2019)

A framework for deep energy-based reinforcement learning with quantum speed-up. - Sofiène Jerbi; Hendrik Poulsen Nautrup; Lea M. Trenkwalder; Hans J. Briegel; Vedran Dunjko (CoRR, 2019)

Optimizing Quantum Error Correction Codes with Reinforcement Learning. - Hendrik Poulsen Nautrup; Nicolas Delfosse; Vedran Dunjko; Hans J. Briegel; Nicolai Friis (Quantum, 2019)

Towards quantum advantage for topological data analysis. - Casper Gyurik; Chris Cade; Vedran Dunjko (CoRR, 2020)

Hybrid divide-and-conquer approach for tree search algorithms. - Mathys Rennela; Alfons Laarman; Vedran Dunjko (CoRR, 2020)

Skill Learning by Autonomous Robotic Playing Using Active Learning and Exploratory Behavior Composition. - Simon Hangl; Vedran Dunjko; Hans J. Briegel; Justus H. Piater (Frontiers Robotics AI, 2020)

On the convergence of projective-simulation-based reinforcement learning in Markov decision processes. - Walter L. Boyajian; Jens Clausen; Lea M. Trenkwalder; Vedran Dunjko; Hans J. Briegel (Quantum Mach. Intell., 2020)

Tabu-Driven Quantum Neighborhood Samplers. - Charles Moussa; Hao Wang 025; Henri Calandra; Thomas Bäck; Vedran Dunjko (EvoCOP, 2021)

Parametrized Quantum Policies for Reinforcement Learning. - Sofiène Jerbi; Casper Gyurik; Simon C. Marshall; Hans J. Briegel; Vedran Dunjko (NeurIPS, 2021)

Reinforcement learning for optimization of variational quantum circuit architectures. - Mateusz Ostaszewski; Lea M. Trenkwalder; Wojciech Masarczyk; Eleanor Scerri; Vedran Dunjko (NeurIPS, 2021)

Variational quantum policies for reinforcement learning. - Sofiène Jerbi; Casper Gyurik; Simon C. Marshall; Hans J. Briegel; Vedran Dunjko (CoRR, 2021)

Quantum machine learning beyond kernel methods. - Sofiène Jerbi; Lukas J. Fiderer; Hendrik Poulsen Nautrup; Jonas M. Kübler; Hans J. Briegel; Vedran Dunjko (CoRR, 2021)

Experimental quantum speed-up in reinforcement learning agents. - Valeria Saggio; Beate E. Asenbeck; Arne Hamann 002; Teodor Strömberg; Peter Schiansky; Vedran Dunjko; Nicolai Friis; Nicholas C. Harris; Michael Hochberg; Dirk R. Englund; Sabine Wölk; Hans J. Briegel; et al. (Nat., 2021)

Quantum-accessible reinforcement learning beyond strictly epochal environments. - Arne Hamann 002; Vedran Dunjko; Sabine Wölk (Quantum Mach. Intell., 2021)

Smooth input preparation for quantum and quantum-inspired machine learning. - Zhikuan Zhao; Jack K. Fitzsimons; Patrick Rebentrost; Vedran Dunjko; Joseph F. Fitzsimons (Quantum Mach. Intell., 2021)

On solving classes of positive-definite quantum linear systems with quadratically improved runtime in the condition number. - Davide Orsucci; Vedran Dunjko (Quantum, 2021)

Encoding strongly-correlated many-boson wavefunctions on a photonic quantum computer: application to the attractive Bose-Hubbard model. - Saad Yalouz; Bruno Senjean; Filippo Miatto; Vedran Dunjko (Quantum, 2021)

Hyperparameter Importance of Quantum Neural Networks Across Small Datasets. - Charles Moussa; Jan N. van Rijn; Thomas Bäck; Vedran Dunjko (DS, 2022)

High Dimensional Quantum Learning With Small Quantum Computers. - Simon C. Marshall; Casper Gyurik; Vedran Dunjko (CoRR, 2022)

Equivariant quantum circuits for learning on weighted graphs. - Andrea Skolik; Michele Cattelan; Sheir Yarkoni; Thomas Bäck; Vedran Dunjko (CoRR, 2022)

Reinforcement Learning Assisted Recursive QAOA. - Yash J. Patel; Sofiène Jerbi; Thomas Bäck; Vedran Dunjko (CoRR, 2022)

On establishing learning separations between classical and quantum machine learning with classical data. - Casper Gyurik; Vedran Dunjko (CoRR, 2022)

Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning. - Andrea Skolik; Sofiène Jerbi; Vedran Dunjko (Quantum, 2022)

Towards quantum advantage via topological data analysis. - Casper Gyurik; Chris Cade; Vedran Dunjko (Quantum, 2022)

Continuous Variables Quantum Algorithm for Solving Ordinary Differential Equations. - Alice Barthe; Michele Grossi; Jordi Tura; Vedran Dunjko (QCE, 2023)

Application of quantum-inspired generative models to small molecular datasets. - Charles Moussa; Hao Wang 025; Mauricio Araya-Polo; Thomas Bäck; Vedran Dunjko (QCE, 2023)

Benchmarking Adaptive Quantum Circuit Optimization Algorithms for Quantum Chemistry. - Waheeda Saib; Xavier Bonet-Monroig; Vedran Dunjko; Ivano Tavernelli; Thomas Bäck; Hao Wang 025 (QCE, 2023)

Quantum Policy Gradient Algorithms. - Sofiène Jerbi; Arjan Cornelissen; Maris Ozols; Vedran Dunjko (TQC, 2023)

Shadows of quantum machine learning. - Sofiène Jerbi; Casper Gyurik; Simon C. Marshall; Riccardo Molteni; Vedran Dunjko (CoRR, 2023)

Enhancing variational quantum state diagonalization using reinforcement learning techniques. - Akash Kundu; Przemyslaw Bedelek; Mateusz Ostaszewski; Onur Danaci; Yash J. Patel; Vedran Dunjko; Jaroslaw Adam Miszczak (CoRR, 2023)

Exponential separations between classical and quantum learners. - Casper Gyurik; Vedran Dunjko (CoRR, 2023)

Compilation of product-formula Hamiltonian simulation via reinforcement learning. - Lea M. Trenkwalder; Eleanor Scerri; Thomas E. O'Brien; Vedran Dunjko (CoRR, 2023)

Structural risk minimization for quantum linear classifiers. - Casper Gyurik; Dyon van Vreumingen; Vedran Dunjko (Quantum, 2023)

Hybrid divide-and-conquer approach for tree search algorithms. - Mathys Rennela; Sebastiaan Brand; Alfons Laarman; Vedran Dunjko (Quantum, 2023)

High Dimensional Quantum Machine Learning With Small Quantum Computers. - Simon C. Marshall; Casper Gyurik; Vedran Dunjko (Quantum, 2023)

LIMDD: A Decision Diagram for Simulation of Quantum Computing Including Stabilizer States. - Lieuwe Vinkhuijzen; Tim Coopmans; David Elkouss; Vedran Dunjko; Alfons Laarman (Quantum, 2023)

Curriculum reinforcement learning for quantum architecture search under hardware errors. - Yash J. Patel; Akash Kundu; Mateusz Ostaszewski; Xavier Bonet-Monroig; Vedran Dunjko; Onur Danaci (ICLR, 2024)

Reliable Classifications with Guaranteed Confidence Using the Dempster-Shafer Theory of Evidence. - Marie C. Kempkes; Vedran Dunjko; Evert P. L. van Nieuwenburg; Jakob Spiegelberg (ECML/PKDD (2), 2024)

On Bounded Advice Classes. - Simon C. Marshall; Casper Gyurik; Vedran Dunjko (CoRR, 2024)

Quantum computing and persistence in topological data analysis. - Casper Gyurik; Alexander Schmidhuber; Robbie King; Vedran Dunjko; Ryu Hayakawa (CoRR, 2024)

On the expressivity of embedding quantum kernels. - Elies Gil-Fuster; Jens Eisert; Vedran Dunjko (Mach. Learn. Sci. Technol., 2024)

Hyperparameter importance and optimization of quantum neural networks across small datasets. - Charles Moussa; Yash J. Patel; Vedran Dunjko; Thomas Bäck; Jan N. van Rijn (Mach. Learn., 2024)

Improved Separation Between Quantum and Classical Computers for Sampling and Functional Tasks. - Simon C. Marshall; Scott Aaronson; Vedran Dunjko (CCC, 2025)

On the Relation between Trainability and Dequantization of Variational Quantum Learning Models. - Elies Gil-Fuster; Casper Gyurik; Adrián Pérez-Salinas; Vedran Dunjko (ICLR, 2025)

Approximation and Generalization Capacities of Parametrized Quantum Circuits for Functions in Sobolev Spaces. - Alberto Manzano; David Dechant; Jordi Tura; Vedran Dunjko (Quantum, 2025)

Double descent in quantum machine learning. - Marie C. Kempkes; Aroosa Ijaz; Elies Gil-Fuster; Carlos Bravo-Prieto; Jakob Spiegelberg; Evert P. L. van Nieuwenburg; Vedran Dunjko (CoRR, 2025)

Detecting underdetermination in parameterized quantum circuits. - Marie C. Kempkes; Jakob Spiegelberg; Evert P. L. van Nieuwenburg; Vedran Dunjko (CoRR, 2025)

Quantum computing and artificial intelligence: status and perspectives. - Giovanni Acampora; Andris Ambainis; Natalia Ares; Leonardo Banchi; Pallavi Bhardwaj; Daniele Binosi; G. Andrew D. Briggs; Tommaso Calarco; Vedran Dunjko; Jens Eisert; Olivier Ezratty; Paul Erker; et al. (CoRR, 2025)