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- Vedran Dunjko
Leiden University, The Netherlands
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Publication search results
found 75 matches
- 2024
- 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) - Elies Gil-Fuster, Jens Eisert, Vedran Dunjko:
On the expressivity of embedding quantum kernels. Mach. Learn. Sci. Technol. 5(2): 25003 (2024) - 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 - 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 - 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) - Simon C. Marshall, Casper Gyurik, Vedran Dunjko:
On Bounded Advice Classes. CoRR abs/2405.18155 (2024) - 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
- Casper Gyurik, Dyon van Vreumingen, Vedran Dunjko:
Structural risk minimization for quantum linear classifiers. Quantum 7: 893 (2023) - Simon C. Marshall, Casper Gyurik, Vedran Dunjko:
High Dimensional Quantum Machine Learning With Small Quantum Computers. Quantum 7: 1078 (2023) - Mathys Rennela, Sebastiaan Brand, Alfons Laarman, Vedran Dunjko:
Hybrid divide-and-conquer approach for tree search algorithms. Quantum 7: 959 (2023) - 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) - Alice Barthe, Michele Grossi, Jordi Tura, Vedran Dunjko:
Continuous Variables Quantum Algorithm for Solving Ordinary Differential Equations. QCE 2023: 48-53 - 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 - 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 - Sofiène Jerbi, Arjan Cornelissen, Maris Ozols, Vedran Dunjko:
Quantum Policy Gradient Algorithms. TQC 2023: 13:1-13:24 - 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) - Sofiène Jerbi, Casper Gyurik, Simon C. Marshall, Riccardo Molteni, Vedran Dunjko:
Shadows of quantum machine learning. CoRR abs/2306.00061 (2023) - 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) - Casper Gyurik, Vedran Dunjko:
Exponential separations between classical and quantum learners. CoRR abs/2306.16028 (2023) - Elies Gil-Fuster, Jens Eisert, Vedran Dunjko:
On the expressivity of embedding quantum kernels. CoRR abs/2309.14419 (2023) - 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
- Casper Gyurik, Chris Cade, Vedran Dunjko:
Towards quantum advantage via topological data analysis. Quantum 6: 855 (2022) - Andrea Skolik, Sofiène Jerbi, Vedran Dunjko:
Quantum agents in the Gym: a variational quantum algorithm for deep Q-learning. Quantum 6: 720 (2022) - Charles Moussa, Jan N. van Rijn, Thomas Bäck, Vedran Dunjko:
Hyperparameter Importance of Quantum Neural Networks Across Small Datasets. DS 2022: 32-46 - Simon C. Marshall, Casper Gyurik, Vedran Dunjko:
High Dimensional Quantum Learning With Small Quantum Computers. CoRR abs/2203.13739 (2022) - Andrea Skolik, Michele Cattelan, Sheir Yarkoni, Thomas Bäck, Vedran Dunjko:
Equivariant quantum circuits for learning on weighted graphs. CoRR abs/2205.06109 (2022) - Charles Moussa, Jan N. van Rijn, Thomas Bäck, Vedran Dunjko:
Hyperparameter Importance of Quantum Neural Networks Across Small Datasets. CoRR abs/2206.09992 (2022) - Yash J. Patel, Sofiène Jerbi, Thomas Bäck, Vedran Dunjko:
Reinforcement Learning Assisted Recursive QAOA. CoRR abs/2207.06294 (2022) - Casper Gyurik, Vedran Dunjko:
On establishing learning separations between classical and quantum machine learning with classical data. CoRR abs/2208.06339 (2022) - Sofiène Jerbi, Arjan Cornelissen, Maris Ozols, Vedran Dunjko:
Quantum policy gradient algorithms. CoRR abs/2212.09328 (2022)
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