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Vedran Dunjko

  1. Parametrized Quantum Policies for Reinforcement Learning
    2021/03/09 by Sofiène Jerbi, Casper Gyurik, Jerbi, Sofiene +7 · 25 citations
    Computer Science · Physics and Astronomy · #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Mechanics and Applications
  2. Reinforcement learning for optimization of variational quantum circuit architectures
    2021/03/30 by Mateusz Ostaszewski, Ostaszewski, Mateusz, Lea M. Trenkwalder +7 · 16 citations
    Computer Science · Engineering · #Quantum Computing Algorithms and Architecture #Advancements in Semiconductor Devices and Circuit Design #Advanced Memory and Neural Computing
  3. Quantum computing and artificial intelligence: status and perspectives
    2025/05/29 by Giovanni Acampora, Acampora, Giovanni, Andris Ambainis +75 · 6 voices · 6 citations
    Computer Science · Physics and Astronomy · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Quantum Physics (quant-ph) #cs.AI #cs.LG #quant-ph
  4. Equivariant quantum circuits for learning on weighted graphs
    2022/05/12 by Andrea Skolik, Michele Cattelan, Skolik, Andrea +7 · 11 citations
    Computer Science · Physics and Astronomy · #Quantum Computing Algorithms and Architecture #Quantum and electron transport phenomena #Quantum Information and Cryptography
  5. Curriculum reinforcement learning for quantum architecture search under hardware errors
    2024/02/05 by Yash J. Patel, Akash Kundu, Patel, Yash J. +9 · 12 citations
    Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Quantum Information and Cryptography #Quantum Physics (quant-ph)
  6. On the relation between trainability and dequantization of variational quantum learning models
    2024/06/11 by Elies Gil-Fuster, Gil-Fuster, Elies, Casper Gyurik +5 · 1 voice · 8 citations
    Physics and Astronomy · #Statistical Mechanics and Entropy
  7. Robustness of quantum reinforcement learning under hardware errors
    2022/12/19 by Andrea Skolik, Skolik, Andrea, Stefano Mangini +7 · 5 citations
    Computer Science · #Quantum Computing Algorithms and Architecture #Neural Networks and Reservoir Computing #Quantum Information and Cryptography
  8. Exponential improvements for quantum-accessible reinforcement learning
    2017/10/30 by Vedran Dunjko, Yi-Kai Liu, Dunjko, Vedran +5 · 3 citations
    Computer Science · #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Computability, Logic, AI Algorithms
  9. Exponential separations between classical and quantum learners
    2023/06/28 by Casper Gyurik, Vedran Dunjko, Gyurik, Casper +1 · 5 citations
    Computer Science · Physics and Astronomy · #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Mechanics and Applications #Quantum Physics (quant-ph)
  10. Framework for learning agents in quantum environments
    2015/07/30 by Vedran Dunjko, Jacob M. Taylor, Dunjko, Vedran +3 · 2 citations
    Computer Science · #Artificial Intelligence (cs.AI) #Computability, Logic, AI Algorithms #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms #Quantum Computing Algorithms and Architecture #Quantum Physics (quant-ph)
  11. Parameterized quantum circuits as universal generative models for continuous multivariate distributions
    2024/02/15 by Alice Barthe, Barthe, Alice, Michele Grossi +7 · 5 citations
    Computer Science · #Quantum Computing Algorithms and Architecture #Evolutionary Algorithms and Applications
  12. Universal approximation of continuous functions with minimal quantum circuits
    2024/11/28 by Adrián Pérez-Salinas, Mahtab Yaghubi Rad, Pérez-Salinas, Adrián +5 · 6 citations
    Computer Science · Mathematics · #Quantum Computing Algorithms and Architecture #Numerical Methods and Algorithms #Mathematical Approximation and Integration
  13. Modern applications of machine learning in quantum sciences
    2022/04/08 by Anna Dawid, Julian Arnold, Dawid, Anna +55 · 3 citations
    Computer Science · Materials Science · #Disordered Systems and Neural Networks (cond-mat.dis-nn) #FOS: Physical sciences #Machine Learning in Materials Science #Mesoscale and Nanoscale Physics (cond-mat.mes-hall) #Quantum Computing Algorithms and Architecture #Quantum Physics (quant-ph)
  14. On establishing learning separations between classical and quantum machine learning with classical data
    2022/08/12 by Casper Gyurik, Vedran Dunjko, Gyurik, Casper +1 · 2 citations
    Computer Science · #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Machine Learning and Algorithms #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Physics (quant-ph)
  15. Quantum-enhanced Secure Delegated Classical Computing
    2014/05/18 by Vedran Dunjko, Dunjko, Vedran, Theodoros Kapourniotis +3 · 1 citation
    Computer Science · #Cryptography and Data Security #FOS: Physical sciences #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum Physics (quant-ph)
  16. Universality and kernel-adaptive training for classically trained, quantum-deployed generative models
    2025/10/09 by Andrii Kurkin, Kevin Shen, Kurkin, Andrii +7 · 3 citations
    Computer Science · Physics and Astronomy · #FOS: Physical sciences #Neural Networks and Reservoir Computing #Quantum Computing Algorithms and Architecture #Quantum Physics (quant-ph) #Quantum many-body systems
  17. Quantum machine learning advantages beyond hardness of evaluation
    2025/04/22 by Riccardo Molteni, Molteni, Riccardo, Simon C. Marshall +3 · 2 citations
    Computer Science · Materials Science · #Quantum Computing Algorithms and Architecture #Machine Learning in Materials Science
  18. Testing the presence of balanced and bipartite components in a sparse graph is QMA1-hard
    2024/12/19 by Massimiliano Incudini, Casper Gyurik, Incudini, Massimiliano +5 · 1 citation
    Computer Science · Engineering · #VLSI and Analog Circuit Testing #VLSI and FPGA Design Techniques #Low-power high-performance VLSI design
  19. Cautious optimism for deep parameterized quantum circuits
    2026/07/23 by Marie Kempkes, Elies Gil-Fuster, Carlos Bravo-Prieto +5
    #quant-ph #cs.LG #stat.ML