VD
Vedran Dunjko
quant-phcs.LGcs.AIstat.MLcs.CCcs.DScond-mat.dis-nncs.CRQuantum informationQuantum Theory
On Valency
published · living versionsW_trt7ux8s·v2 · currentpublished
Supervised Learning with Quantum Hamiltonian Embeddings
with Riccardo Molteni, Minjun Jeon, Paolo Braccia, Abhinav Deshpande +6
2 versions
W_gyehtsv4·v1published
Preprints & journals
107 papers in the corpus · 2010–2026Supervised Learning with Quantum Hamiltonian Embeddings2610.26833v2 · Riccardo Molteni, Minjun Jeon, Paolo Braccia et al.2026 · 0 citationsarXivon Valency
Task-aligned spectral inductive biases in parameterized quantum circuits2610.23834v1 · Felix Frohnert, Adri'an P'erez-Salinas, Sebastian Lange et al.2026 · 0 citationsarXiv
Quantifying the Value of Privileged Information Using a PAC-Bayesian Approach2609.12891v1 · Vasily Bokov, Sebastian Schmitt, Vedran Dunjko et al.2026 · 0 citationsarXivon Valency
Cautious optimism for deep parameterized quantum circuits2607.21409v2 · Marie Kempkes, Elies Gil-Fuster, Carlos Bravo-Prieto et al.2026 · 0 citationsarXiv
Provable learning separation for predicting time-evolution of quantum many-body systems2607.06472v1 · Rahul Bandyopadhyay, Riccardo Molteni, Jens Eisert et al.2026 · 0 citationsarXiv
Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits2605.21346v1 · Onur Danaci, Yash J. Patel, Riccardo Molteni et al.2026 · 0 citationsarXiv
Universality of Classically Trainable, Quantum-Deployed Boson-Sampling Generative Models2603.11014v1 · Andrii Kurkin, Ulysse Chabaud, Zolt'an Kolarovszki et al.2026 · 0 citationsarXiv
Characterizing Trainability of Instantaneous Quantum Polynomial Circuit Born Machines2602.11042v2 · Kevin Shen, Susanne Pielawa, Vedran Dunjko et al.2026 · 0 citationsarXiv
Machine learning with minimal use of quantum computers: Provable advantages in Learning Under Quantum Privileged Information (LUQPI)2601.22006v1 · Vasily Bokov, Lisa Kohl, Sebastian Schmitt et al.2026 · 0 citationsarXiv
Universality and kernel-adaptive training for classically trained, quantum-deployed generative models2510.08476v1 · Andrii Kurkin, Kevin Shen, Susanne Pielawa et al.2025 · 0 citationsarXiv
Computational complexity of the homology problem with orientable filtration: MA-completeness2510.07014v1 · Ryu Hayakawa, Casper Gyurik, Mahtab Yaghubi Rad et al.2025 · 0 citationsarXiv
Quantum generative modeling for financial time series with temporal correlations2507.22035v1 · David Dechant, Eliot Schwander, Lucas van Drooge et al.2025 · 3 citationsMachine Learning: Science and Technology 7:015027 (2026)
Quantum computing and artificial intelligence: status and perspectives2505.23860v3 · Giovanni Acampora, Andris Ambainis, Natalia Ares et al.2025 · 2 citationsarXiv
Quantum machine learning advantages beyond hardness of evaluation2504.15964v2 · Riccardo Molteni, Simon C. Marshall, Vedran Dunjko2025 · 0 citationsarXiv
Note on the Universality of Parameterized IQP Circuits with Hidden Units for Generating Probability Distributions2504.05997v1 · Andrii Kurkin, Kevin Shen, Susanne Pielawa et al.2025 · 0 citationsarXiv
Weighted Approximate Quantum Natural Gradient for Variational Quantum Eigensolver2504.04932v2 · Chenyu Shi, Vedran Dunjko, Hao Wang2025 · 1 citation2026 Quantum Sci. Technol. 11 015060
Detecting underdetermination in parameterized quantum circuits2504.03315v1 · Marie Kempkes, Jakob Spiegelberg, Evert van Nieuwenburg et al.2025 · 0 citationsMach. Learn.: Sci. Technol. 7 015010 (2026)
Parameterized quantum circuits as universal generative models for continuous multivariate distributions.40708896 · Barthe, Alice, Grossi, Michele, Vallecorsa, Sofia et al.2026 · 11 citationsNPJ quantum information. 2025;11(1):121
Variational quantum generative modeling by sampling expectation values of tunable observables.41268184 · Shen, Kevin, Kurkin, Andrii, Pérez-Salinas, Adrián et al.2025 · 0 citationsNPJ quantum information. 2025;11(1):178
Testing the presence of balanced and bipartite components in a sparse graph is QMA1-hard2412.14932v1 · Massimiliano Incudini, Casper Gyurik, Riccardo Molteni et al.2024 · 0 citationsarXiv
Error and Resource Estimates of Variational Quantum Algorithms for Solving Differential Equations Based on Runge-Kutta Methods2412.12262v2 · David Dechant, Liubov Markovich, Vedran Dunjko et al.2024 · 0 citationsJ. Math. Phys. 67, 012205 (2026)
Universal approximation of continuous functions with minimal quantum circuits2411.19152v3 · Adri'an P'erez-Salinas, Mahtab Yaghubi Rad, Alice Barthe et al.2024 · 1 citationPhys. Rev. Research 7, 043282, 2025
Multiple-basis representation of quantum states2411.03110v2 · Adri'an P'erez-Salinas, Patrick Emonts, Jordi Tura et al.2024 · 0 citationsarXiv
Improved separation between quantum and classical computers for sampling and functional tasks2410.20935v1 · Simon C. Marshall, Scott Aaronson, Vedran Dunjko2024 · 0 citationsarXiv
Provable quantum speedups for computing persistence in topological data analysis2410.21258v2 · Casper Gyurik, Alexander Schmidhuber, Robbie King et al.2024 · 2 citationsPRX Quantum 7, 020361 (2026)
Trainability maximization using estimation of distribution algorithms assisted by surrogate modelling for quantum architecture search2407.20091v1 · Vicente P. Soloviev, Vedran Dunjko, Concha Bielza et al.2024 · 2 citationsarXiv
Shadows of quantum machine learning.38971826 · Jerbi, Sofiene, Gyurik, Casper, Marshall, Simon C et al.2026 · 71 citationsNature communications. 2024;15(1):5676
On the relation between trainability and dequantization of variational quantum learning models2406.07072v3 · Elies Gil-Fuster, Casper Gyurik, Adri'an P'erez-Salinas et al.2024 · 2 citationsarXiv
On Bounded Advice Classes2405.18155v1 · Simon Marshall, Casper Gyurik, Vedran Dunjko2024 · 0 citationsarXiv
Exponential quantum advantages in learning quantum observables from classical data2405.02027v2 · Riccardo Molteni, Casper Gyurik, Vedran Dunjko2024 · 6 citationsarXiv
Curriculum reinforcement learning for quantum architecture search under hardware errors2402.03500v1 · Yash J. Patel, Akash Kundu, Mateusz Ostaszewski et al.2024 · 11 citationsarXiv
Reinforcement learning assisted recursive QAOA.38261853 · Patel, Yash J, Jerbi, Sofiene, Bäck, Thomas et al.2025 · 29 citationsEPJ quantum technology. 2024;11(1):6
Challenges and Opportunities in Quantum Optimization2312.02279v3 · Amira Abbas, Andris Ambainis, Brandon Augustino et al.2023 · 203 citationsNat Rev Phys (2024)
Limitations of measure-first protocols in quantum machine learning2311.12618v1 · Casper Gyurik, Riccardo Molteni, Vedran Dunjko2023 · 0 citationsarXiv
Compilation of product-formula Hamiltonian simulation via reinforcement learning2311.04285v1 · Lea M. Trenkwalder, Eleanor Scerri, Thomas E. O'Brien et al.2023 · 1 citationarXiv
On the expressivity of embedding quantum kernels2309.14419v2 · Elies Gil-Fuster, Jens Eisert, Vedran Dunjko2023 · 18 citationsMachine Learning: Science and Technology 5, 025003 (2024)
Approximation and Generalization Capacities of Parametrized Quantum Circuits for Functions in Sobolev Spaces2307.14792v3 · Alberto Manzano, David Dechant, Jordi Tura et al.2023 · 6 citationsQuantum 9, 1658 (2025)
Quantum Computing for High-Energy Physics: State of the Art and Challenges. Summary of the QC4HEP Working Group2307.03236v1 · Alberto Di Meglio, Karl Jansen, Ivano Tavernelli et al.2023 · 216 citationsPRX Quantum 5, 037001 (2024)
Exponential separations between classical and quantum learners2306.16028v2 · Casper Gyurik, Vedran Dunjko2023 · 9 citationsarXiv
Enhancing variational quantum state diagonalization using reinforcement learning techniques2306.11086v3 · Akash Kundu, Przemys\law Bede\lek, Mateusz Ostaszewski et al.2023 · 13 citationsNew Journal of Physics, 26, 013034 (2024)
Application of quantum-inspired generative models to small molecular datasets2304.10867v1 · C. Moussa, H. Wang, M. Araya-Polo et al.2023 · 9 citations2023 IEEE International Conference on Quantum Computing and Engineering (QCE), Bellevue, WA, USA, 2023, pp. 342-348
All this for one qubit? Bounds on local circuit cutting schemes2303.13422v1 · Simon C. Marshall, Jordi Tura, Vedran Dunjko2023 · 1 citationarXiv
Bloch Sphere Binary Trees: A method for the visualization of sets of multi-qubit systems pure states2302.02957v1 · Alice Barthe, Michele Grossi, Jordi Tura et al.2023 · 1 citationarXiv
Quantum machine learning beyond kernel methods.36720861 · Jerbi, Sofiene, Fiderer, Lukas J, Poulsen Nautrup, Hendrik et al.2023 · 234 citationsNature communications. 2023;14(1):517
Reduce&chop: Shallow circuits for deeper problems2212.11862v3 · Adri'an P'erez-Salinas, Radoica Draski'c, Jordi Tura et al.2022 · 7 citationsPhys. Rev. A 108, 062423, 2023
Quantum policy gradient algorithms2212.09328v1 · Sofiene Jerbi, Arjan Cornelissen, Māris Ozols et al.2022 · 8 citations18th Conference on the Theory of Quantum Computation, Communication and Cryptography (TQC 2023)
Robustness of quantum reinforcement learning under hardware errors2212.09431v1 · Andrea Skolik, Stefano Mangini, Thomas Back et al.2022 · 29 citationsarXiv
Analyzing Prospects for Quantum Advantage in Topological Data Analysis2209.13581v3 · Dominic W. Berry, Yuan Su, Casper Gyurik et al.2022 · 45 citationsPRX Quantum 5, 010319 (2024)
On establishing learning separations between classical and quantum machine learning with classical data2208.06339v2 · Casper Gyurik, Vedran Dunjko2022 · 8 citationsarXiv
Hyperparameter Importance of Quantum Neural Networks Across Small Datasets2206.09992v1 · Charles Moussa, Jan N. van Rijn, Thomas Back et al.2022 · 10 citationsarXiv
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