Full publication list

Full list of publications (published followed by preprints)

Published: 

       1.     Provable quantum speedups for computing persistence in topological data analysis, C Gyurik, A Schmidhuber, R King, V Dunjko, R Hayakawa, PRX             Quantum 7 (2), 020361 (2026)

2. Quantum machine learning advantages beyond hardness of evaluation,  R Molteni, SC Marshall, V Dunjko, ICLR 2026 (preprint arXiv:2504.15964) (2026)

3. Exponential quantum advantages in learning quantum observables from classical data, R Molteni, C Gyurik, V Dunjko, npj Quantum Information 12, 19 (2026)

4. Quantum generative modeling for financial time series with temporal correlations, D Dechant, E Schwander, L Van Drooge, C Moussa, D Garlaschelli, V Dunjko, J Tura, Machine Learning: Science and Technology 7 (1), 015027 (2026)

5. Detecting underdetermination in parameterized quantum circuits, M Kempkes, J Spiegelberg, E van Nieuwenburg, V Dunjko, Machine Learning: Science and Technology 7 (1), 015010 (2026)

6. Double descent in quantum kernel methods, M Kempkes, A Ijaz, E Gil-Fuster, C Bravo-Prieto, J Spiegelberg, E van Nieuwenburg, V Dunjko, PRX Quantum 7 (1), 010312 (2026)

7. Weighted approximate quantum natural gradient for variational quantum eigensolver, C Shi, V Dunjko, H Wang, Quantum Science and Technology 11, 015060 (2026)

8. Error and resource estimates of variational quantum algorithms for solving differential equations based on Runge-Kutta methods, D Dechant, L Markovich, V Dunjko, J Tura, Journal of Mathematical Physics 67 (1) (2026)

9. Universal approximation of continuous functions with minimal quantum circuits, A Pérez-Salinas, M Yaghubi Rad, A Barthe, V Dunjko, Physical Review Research 7 (4), 043282 (2025)

10.  Variational quantum generative modeling by sampling expectation values of tunable observables, K Shen, A Kurkin, A Pérez-Salinas, E Shishenina, V Dunjko, H Wang, npj Quantum Information 11 (1), 178 (2025)

11.  Limitations of measure-first protocols in quantum machine learning, C Gyurik, R Molteni, V Dunjko, ICML (preprint arXiv:2311.12618) (2025)

12.  On the relation between trainability and dequantization of variational quantum learning models, E Gil-Fuster, C Gyurik, A Pérez-Salinas, V Dunjko, ICLR 2025 (preprint arXiv:2406.07072) (2025)

13.  Parameterized quantum circuits as universal generative models for continuous multivariate distributions, A Barthe, M Grossi, S Vallecorsa, J Tura, V Dunjko,. npj Quantum Information 11 (1), 121 (2025)

14.  Approximation and Generalization Capacities of Parametrized Quantum Circuits for Functions in Sobolev Spaces, A Manzano, D Dechant, J Tura, V Dunjko, Quantum 9, 1658 (2025)

15.  Trainability maximization using estimation of distribution algorithms assisted by surrogate modelling for quantum architecture search, VP Soloviev, V Dunjko, C Bielza, P Larrañaga, H Wang, EPJ Quantum Technology 11 (1), 69 (2024)

16.  Reinforcement learning assisted recursive QAOA, YJ Patel, S Jerbi, T Bäck, V Dunjko, EPJ Quantum Technology 11 (1), 6 (2024)

17.  Challenges and opportunities in quantum optimization, A Abbas, ..., V. Dunjko,  et al. Nature Reviews Physics, 1-18 (2024)

18.  Reliable Classifications with Guaranteed Confidence Using the Dempster-Shafer Theory of Evidence, MC Kempkes, V Dunjko, E van Nieuwenburg, J Spiegelberg, ECML PKDD (2024)

19.  Quantum computing for high-energy physics: state of the art and challenges, A Di Meglio, ..., V Dunjko,... et al. PRX Quantum 5 (3), 037001 (2024)

20.  Shadows of quantum machine learning, S Jerbi, C Gyurik, SC Marshall, R Molteni, V Dunjko, Nature Communications 15 (1), 5676 (2024)

21.  On the expressivity of embedding quantum kernels, E Gil-Fuster, J Eisert, V Dunjko, Machine Learning: Science and Technology 5 (2), 025003 (2024)

22.  Hyperparameter importance and optimization of quantum neural networks across small datasets, C Moussa, YJ Patel, V Dunjko, T Bäck, JN van Rijn, Machine Learning 113 (4), 1941-1966 (2024)

23.  Curriculum reinforcement learning for quantum architecture search under hardware errors, YJ Patel, A Kundu, M Ostaszewski, X Bonet-Monroig, V Dunjko, O Danaci, ICLR 2024 (2024)

24.  Enhancing variational quantum state diagonalization using reinforcement learning techniques, A Kundu, ... V Dunjko, et al., New Journal of Physics 26 (1), 013034 (2024)

25.  Analyzing Prospects of Quantum Advantage in Topological Data Analysis, Dominic W. Berry, Yuan Su, Casper Gyurik, Robbie King, Joao Basso, Alexander Del Toro Barba, Abhishek Rajput, Nathan Wiebe, Vedran Dunjko, Ryan Babbush, PRX Quantum 5, 010319 (2024)

26.  Shallow quantum circuits for deeper problems,  A Pérez-Salinas, R Draškić, J Tura, V Dunjko, Physical Review A 108 (6), 062423 (2023)

27.  Robustness of quantum reinforcement learning under hardware errors, A Skolik, S Mangini, T Bäck, C Macchiavello, V Dunjko, EPJ Quantum Technology 10 (1), 1-43 (2023)

28.  Continuous variables quantum algorithm for solving ordinary differential equations, A Barthe, M Grossi, J Tura, V Dunjko, IEEE International Conference on Quantum Computing and Engineering (QCE) (2023)

29.  Benchmarking Adaptive Quantum Circuit Optimization Algorithms for Quantum Chemistry, W Saib, X Bonet-Monroig, V Dunjko, I Tavernelli, T Bäck, H Wang, IEEE International Conference on Quantum Computing and Engineering (QCE) (2023)

30.  Application of quantum-inspired generative models to small molecular datasets, C Moussa, H Wang, M Araya-Polo, T Bäck, V Dunjko, IEEE International Conference on Quantum Computing and Engineering (QCE) (2023)

31.  LIMDD: A decision diagram for simulation of quantum computing including stabilizer states, L Vinkhuijzen, T Coopmans, D Elkouss, V Dunjko, A Laarman, Quantum 7, 1108 (2023)

32.  High dimensional quantum machine learning with small quantum computers, SC Marshall, C Gyurik, V Dunjko, Quantum 7, 1078 (2023)

33.  Equivariant quantum circuits for learning on weighted graphs, A Skolik, M Cattelan, S Yarkoni, T Bäck, V Dunjko, npj Quantum Information 9 (1), 47 (2023)

34.  Hybrid divide-and-conquer approach for tree search algorithms, M Rennela, S Brand, A Laarman, V Dunjko, Quantum 7, 959 (2023)

35.  Quantum policy gradient algorithms, Sofiene Jerbi, Arjan Cornelissen, Maris Ozols, Vedran Dunjko, TQC (2023)

36.  Performance comparison of optimization methods on variational quantum algorithms, X Bonet-Monroig, ..., V. Dunjko, et al., Physical Review A 107 (3), 032407 (2023)

37.  Certificates of quantum many-body properties assisted by machine learning, Borja Requena, Gorka Muñoz-Gil, Maciej Lewenstein, Vedran Dunjko, Jordi Tura, Physical Review Research 5 (1), 01309 (2023)

38.  Quantum machine learning beyond kernel methods,  Sofiene Jerbi, Lucas J Fiderer, Hendrik P Nautrup, Jonas M Kübler, Hans J Briegel, Vedran Dunjko, Nature Communications 14 (1), 517 (2023)

39.  Structural risk minimization for quantum linear classifiers, Casper Gyurik, Dyon van Vreumingen, Vedran Dunjko, Quantum 7, 893 (2023)

40.  Towards quantum advantage for topological data analysis, Casper Gyurik, Chris Cade, Vedran Dunjko, Quantum 6, 855 (2022)

41.  Unsupervised strategies for identifying optimal parameters in Quantum Approximate Optimization Algorithm, Charles Moussa, Hao Wang, Thomas Bäck, Vedran Dunjko, EPJ Quantum Technology 9 (1), 11 (2022)

42.  Quantum learning unravels quantum system, Vedran Dunjko, Science 376 (6598), 1154-1155 (2022)

43.  Quantum agents in the gym: a variational quantum algorithm for deep q-learning, Andrea Skolik, Sofiene Jerbi, Vedran Dunjko, Quantum 6, 720 (2022)

44.  Parametrized quantum policies for reinforcement learning, Sofiene Jerbi, Casper Gyurik, Simon Marshall, Hans Briegel, Vedran Dunjko,  Advances in Neural Information Processing Systems (NeurIPS) 34, 28362-28375 (2021)

45.  Reinforcement learning for optimization of variational quantum circuit architectures, Mateusz Ostaszewski, Lea M. Trenkwalder, Wojtek Masarczyk, Eleanor Scerri, Vedran Dunjko, Advances in Neural Information Processing Systems (NeurIPS) 34, 18182-18194 (2021)

46.  Inside quantum black boxes, Vedran Dunjko. Nature Physics https://doi.org/10.1038/s41567-021-01246-8 (2021)

47.  Quantum-accessible reinforcement learning beyond strictly epochal environments, Arne Hamann, Vedran Dunjko, Sabine Wölk, Quantum Machine Intelligence 3 (2), 1-18 (2021)

48.  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 5, 57 (2021)

49.  On solving classes of positive-definite quantum linear systems with quadratically improved runtime in the condition number, Davide Orsucci, Vedran Dunjko, Quantum 5, 573 (2021)

50.  Smooth input preparation for quantum and quantum-inspired machine learning,  Zhikuan Zhao, Jack Fitzsimons, Patrick Rebentrost, Vedran Dunjko, Joseph Fitzsimons, Quant. Mach. Int. 3 (1), 1-6 (2021)

51.  Tabu-driven Quantum Neighborhood Samplers, Charles Moussa, Hao Wang, Henri Calandra, Thomas Back, Vedran Dunjko, accepted EvoStar 2021 (2021)

52.  Experimental quantum speed-up in reinforcement learning agents, Valeria Saggio, Beate E Asenbeck, Arne Hamann, Teodor Stromberg, Peter Schiansky, Vedran Dunjko, Nicolai Friis, Nicholas C Harris, Michael Hochberg, Dirk Englund, Sabine Wolk, Hans J Briegel, Philip Walther, Nature 591 (7849), 229-233 (2021)

53.  Quantum enhancements for deep reinforcement learning in large spaces, Sofiene Jerbi, Lea M Trenkwalder, Hendrik Poulsen Nautrup, Hans J Briegel, Vedran Dunjko, PRX Quantum 2 (1), 010328 (2021)

54.  To quantum or not to quantum: towards algorithm selection in near-term quantum optimization, Charles Moussa, Henri Calandra, Vedran Dunjko, Quant. Sci. Tech. 5 (4), 044009 (2020)

55.  On the convergence of projective-simulation-based reinforcement learning in Markov decision processes, Jens Clausen, Walter L. Boyajian, Lea M. Trenkwalder, Vedran Dunjko, Hans J. Briegel Quant. Mach. Int. 13 (2020)

56.  A non-review of Quantum Machine Learning: trends and explorations, Vedran Dunjko, Peter Wittek, Quantum Views 4, 32 (2020)

57.  A hybrid algorithm framework for small quantum computers with application to finding Hamiltonian cycles, Yimin Ge, Vedran Dunjko, J. Math.Phys. 61 (1), 012201 (2020)

58.  Skill Learning by Autonomous Robotic Playing Using Active Learning and Exploratory Behavior Composition, Simon Hangl, Vedran Dunjko, Hans J. Briegel, Justus Piater Front. Robot. AI, 03 April (2020)

59.  Optimizing quantum error correction codes with reinforcement learning, Hendrik Poulsen Nautrup, Nicolas Delfosse, Vedran Dunjko, Hans J Briegel, Nicolai Friis, Quantum 3, 215 (2019)

60.  Simple proof of confidentiality for private quantum channels in noisy environments, Alexander Pirker, Michael Zwerger, Vedran Dunjko, Hans J. Briegel, Wolfgang Dür, Quant, Sci. Techn., 4, 2 (2019)

61.  Speeding-up the decision making of a learning agent using an ion trap quantum processor, Theeraphot Sriarunothai, Sabine Wolk, Gouri Shankar Giri, Nicolai Friis, Vedran Dunjko, Hans J. Briegel, Christof Wunderlich, Quant. Sci. Techn. 4, 015014 (2019)

62.  Computational speedups using small quantum devices. Vedran Dunjko, Yimin Ge and J. Ignacio Cirac, Phys. Rev. Lett. 121, 250501 (Editor’s suggestion, Featured in Physics) (2018)

63.  Neural Network Operations and Susuki-Trotter evolution of Neural Network States, Nahuel Freitas, Giovanna Morigi, Vedran Dunjko, Int. J. Quantum Inf. 16, 1840008 (2018)

64.  Optimal sequential quantum mixing for slowly evolving sequences of Markov chains, Davide Orsucci, Hans J. Briegel and Vedran Dunjko, Quantum 2, 105 (2018)

65.  Machine learning & arti cial intelligence in the quantum domain: a review of recent progress, Vedran Dunjko and Hans J. Briegel Rep., Prog. Phys 81, 074001 (2018)

66.  Active learning machine learns to create new quantum experiments, Alexey A. Melnikov, Hendrik Poulsen Nautrup, Mario Krenn, Vedran Dunjko, Markus Tiersch, Anton Zeilinger, Hans J. Briegel, Proc. Natl. Acad. Sci. 115 (6) pp. 1221-1226 (PNAS Cozzarelli Prize)  (2018)

67.  Long-range big quantum-data transmission, Michael Zwerger, Alexander Pirker, Vedran Dunjko, Wofgang Dur, Hans J. Briegel, Phys. Rev. Lett. 120, 030503 (2018)

68.  Advances in quantum reinforcement learning, Vedran Dunjko, Jacob M. Taylor, Hans J. Briegel, IEEE SMC, Banff, AB, 2017, pp. 282-287. doi: 10.1109/SMC.2017.8122616 (2017).

69.  Projective simulation with generalization, Alexey A. Melnikov, Adi Makmal, Vedran Dunjko, Hans J. Briegel, Sci. Rep. 7, 14430 (2017)

70.  Entanglement generation secure against general attacks, Alexander Pirker, Vedran Dunjko, Wolfgang Dür, Hans J. Briegel, New J. Phys. 19, 113012 (2017)

71.  Flexible resources for quantum metrology, Nicolai Friis, Davide Orsucci, Michalis Skotiniotis, Pavel Sekatski, Vedran Dunjko, Hans J. Briegel, Wolfgang Dür, New J. Phys.,19, 063044 (2017)

72.  Quantum-enhanced machine learning, Vedran Dunjko, Jacob M. Taylor, Hans J. Briegel. Phys. Rev. Lett. 117, 130501 (2016)

73.  Meta-learning within Projective Simulation, Adi Makmal, Alexey A. Melnikov, Vedran Dunjko, Hans J. Briegel, IEEE Access 4, 2110 (2016)

74.  Enhanced delegated computing using coherence, Stefanie Barz, Vedran Dunjko, Florian Schlederer, Merritt Moore, Elham Kashefi, Ian A. Walmsley, Phys. Rev. A 93, 032339 (2016)

75.  Quantum-enhanced Secure Delegated Classical Computing, Vedran Dunjko, Theodoros Kapourniotis, Elham Kashefi, Quant. Inf. Comput. 16, pp 61-86 (2016)

76.  Experimental demonstration of kilometer-range quantum digital signatures, Ross J. Donaldson, Robert J. Collins, Klaudia Kleczkowska, Ryan Amiri, Petros Wallden, Vedran Dunjko, John Jeffers, Erika Andersson, Gerald S. Buller, Phys. Rev. A 93, 012329, (Editors’ suggestion) (2016)

77.  Quantum mixing of Markov chains for special distributions, Vedran Dunjko, Hans J. Briegel, New J. Phys. 17, 073004 (2015)

78.  Quantum digital signatures with quantum-key-distribution components, Petros Wallden, Vedran Dunjko, Adrian Kent, Erika Andersson, Phys. Rev. A 91, 042304 (2015)

79.  Quantum-enhanced deliberation of learning agents in trapped ions, Vedran Dunjko, Nicolai Friis, Hans J. Briegel, New J. Phys. 17, 023006 (2015)

80.  Ground state blind quantum computation on AKLT state, Tomoyuki Morimae, Vedran Dunjko, Elham Kashefi, Quantum Inf. Comput. 15, 3&4, pp 200-234 (2015)

81.  Entanglement of pi-locally-maximally-entanglable states and the satis ability problem, Adi Makmal, Markus Tiersch, Vedran Dunjko, Shengjun Wu, Phys. Rev. A 90, 042308 (2014)

82.  Quantum speed-up for active learning agents, Giuseppe Davide Paparo, Vedran Dunjko, Adi Makmal, Miguel Angel Martin-Delgado, Hans J. Briegel, Phys. Rev. X 4, 031002 (2014)

83.  Implementing quantum control for unknown subroutines, Nicolai Friis, Vedran Dunjko, Wolfgang Dür, Hans J. Briegel Phys., Rev A 89, 030303(R) (2014)

84.  Optical realisation of Quantum Digital Signatures without quantum memory. Robert J. Collins, Ross J. Donaldson, Vedran Dunjko, Petros Wallden, Patrick J. Clarke, Erika Andersson, John Je ers, Gerald S. Buller, Phys. Rev. Lett. 113, 040502, (Editors’ Suggestion; Featured in Physics) (2014)

85.  Minimum-cost quantum measurements for quantum information, Petros Wallden, Vedran Dunjko, Erika Andersson, J. Phys. A 47 125303 (2014)

86.  Quantum Digital Signatures without Quantum Memory, Vedran Dunjko, Petros Wallden, Erika Andersson, Phys. Rev. Lett. 112 040502 (2014)

87.  Composable security of delegated quantum computation, Vedran Dunjko, Joseph F. Fitzsimons, Christopher Portmann, Renato Renner, Lecture Notes in Computer Science 8874 pp 406-425 (Asiacrypt 2014) (2014)

88.  Phylostratigraphic profiles reveal a deep evolutionary history of the vertebrate head sensory systems, Martin Sebastijan Šestak, Vedran Božičević, Robert Bakarić, Vedran Dunjko, Tomislav Domazet-Lošo, Front. Zool. 10:18 (2013)

89.  Extended phase map decompositions for unitaries, Vedran Dunjko, Elham Kashefi, Math. Struct. Comput. Sci., 23, pp 360-385, (2013)

90.  Experimental demonstration of quantum digital signatures, Patrick J. Clarke, Robert J. Collins, Vedran Dunjko, Erika Andersson, John Jeffers, Gerald S. Buller, Nat. Commun. 3:1174 (2012)

91.  Truly noiseless probabilistic amplification, Vedran Dunjko, Erika Andersson, Phys. Rev. A 86 042322 (2012)

92.  Transformations between symmetric sets of quantum states, Vedran Dunjko, Erika Andersson, J. Phys. A 45 365304 (2012)

93.  Universal blind quantum computing with weak coherent pulses, Vedran Dunjko, Elham Kashefi, Anthony Leverrier, Phys. Rev. Lett. 108 200502 (2012)

94.  Novel modifications of parallel Jacobi algorithms, Sanja Singer, Saša Singer, Vedran Novaković, Aleksandar Ušćumlić, Vedran Dunjko, Numer. Algorithms 59 1-27 (2012)

95.  Algebraic characterisation of one-way patterns Vedran Dunjko, Elham Kashefi, In Proc. 6th Workshop on Developments in Computational Models: Causality, Computation, and Physics 26 EPTCS, pp 85-100 (2010)

Submitted and preprints

96.     Quantifying the Value of Privileged Information Using a PAC-Bayesian Approach, V Bokov, S Schmitt, V Dunjko, H Wang, arXiv preprint arXiv:2609.12891 (2026)

97.     Cautious optimism for deep parameterized quantum circuits, M Kempkes, E Gil-Fuster, C Bravo-Prieto, A Ijaz, A Wilms, J Eisert, E van Nieuwenburg, V Dunjko, arXiv preprint arXiv:2607.21409 (2026)

98.     Provable learning separation for predicting time-evolution of quantum many-body systems, R Bandyopadhyay, R Molteni, J Eisert, V Dunjko, S Jerbi, arXiv preprint arXiv:2607.06472 (2026)

99.     Evidence of quantum machine learning advantage with tens of noisy qubits, O Danaci, YJ Patel, R Molteni, E van Nieuwenburg, V Dunjko, JA Krzywda, arXiv preprint arXiv:2605.21346 (2026)

100.  Universality of Classically Trainable, Quantum-Deployed Boson-Sampling Generative Models, A Kurkin, U Chabaud, Z Kolarovszki, B Bakó, Z Zimborás, V Dunjko, arXiv preprint arXiv:2603.11014 (2026)

101.  Characterizing Trainability of Instantaneous Quantum Polynomial Circuit Born Machines, K Shen, S Pielawa, V Dunjko, H Wang, arXiv preprint arXiv:2602.11042 (2026)

102.  Machine learning with minimal use of quantum computers: Provable advantages in Learning Under Quantum Privileged Information (LUQPI), V Bokov, L Kohl, S Schmitt, V Dunjko, arXiv preprint arXiv:2601.22006 (2026)

103.  Universality and kernel-adaptive training for classically trained, quantum-deployed generative models, A Kurkin, K Shen, S Pielawa, H Wang, V Dunjko, arXiv preprint arXiv:2510.08476 (2025)

104.  Computational complexity of the homology problem with orientable filtration: MA-completeness, R Hayakawa, C Gyurik, MY Rad, V Dunjko, arXiv preprint arXiv:2510.07014 (2025)

105.  Quantum Advantage in Learning Quantum Dynamics via Fourier coefficient extraction,  A Barthe, MY Rad, M Grossi, V Dunjko, arXiv preprint arXiv:2506.17089 (2025)

106.  Quantum computing and artificial intelligence: status and perspectives, G Acampora, ... V Dunjko, et al.,  arXiv preprint arXiv:2505.23860 (2025)

107.  Note on the Universality of Parameterized IQP Circuits with Hidden Units for Generating Probability Distributions, A Kurkin, K Shen, S Pielawa, H Wang, V Dunjko,  arXiv preprint arXiv:2504.05997 (2025)

108.  Testing the presence of balanced and bipartite components in a sparse graph is QMA1-hard, M Incudini, C Gyurik, R Molteni, V Dunjko, arXiv preprint arXiv:2412.14932 (2024)

109.  Multiple-basis representation of quantum states, A Pérez-Salinas, P Emonts, J Tura, V Dunjko, arXiv preprint arXiv:2411.03110 (2024)

110.  Improved separation between quantum and classical computers for sampling and functional tasks, SC Marshall, S Aaronson, V Dunjko, arXiv preprint arXiv:2410.20935 (2024)

111.  On Bounded Advice Classes, S Marshall, C Gyurik, V Dunjko, arXiv preprint arXiv:2405.18155 (2024)

112.  Compilation of product-formula Hamiltonian simulation via reinforcement learning, LM Trenkwalder, E Scerri, TE O'Brien, V Dunjko, arXiv preprint arXiv:2311.04285 (2023)

113.  Exponential separations between classical and quantum learners, C Gyurik, V Dunjko, arXiv preprint arXiv:2306.16028 (2023)

114.  All this for one qubit? Bounds on local circuit cutting schemes, SC Marshall, J Tura, V Dunjko, arXiv preprint arXiv:2303.13422 (2023)

115.  Bloch Sphere Binary Trees: A method for the visualization of sets of multi-qubit systems pure states, Alice Barthe, Michele Grossi, Jordi Tura, Vedran Dunjko, arXiv preprint arXiv:2302.02957 (2023)

116.  On establishing learning separations between classical and quantum machine learning with classical data, Casper Gyurik, Vedran Dunjko, arXiv preprint arXiv:2208.06339 (2022)

117.  Modern applications of machine learning in quantum sciences, Anna Dawid et al, arXiv preprint arXiv:2204.04198 (2022)

118.  Tensorflow quantum: A software framework for quantum machine learning, Michael Broughton et al., arXiv preprint arXiv:2003.02989 (2020)

119.  Exponential improvements for quantum-accessible reinforcement learning, Vedran Dunjko, Yi-Kai Liu, Xingyao Wu, Jacob M. Taylor, arXiv preprint arXiv:1710.11160 (2017)

120.  Blind quantum computing with two almost identical states, Vedran Dunjko, Elham Kashefi, arXiv:1604.01586 (2016)

121.  Framework for learning agents in quantum environments, Vedran Dunjko, Jacob M. Taylor, Hans J. Briegel, arXiv:1507.08482 (2015)

122.  On optimising quantum communication in verifiable quantum computing, Theodoros Kapourniotis, Vedran Dunjko, Elham Kashefi, arXiv:1506.06943 [presented at AQIS 2015] (2015)