462 citations · 4.5k across the 108 of their papers we have counts for
6 papers · 1 filter
Optimizing Quantum Error Correction Codes with Reinforcement Learning
Hendrik Poulsen Nautrup, Nicolas Delfosse, Vedran Dunjko +2
Quantum error correction is widely thought to be the key to fault-tolerant quantum computation. However, determining the most suited encoding for unknown error channels or specific…
Advances in Quantum Reinforcement Learning
Vedran Dunjko, Jacob M. Taylor, Hans J. Briegel
In recent times, there has been much interest in quantum enhancements of machine learning, specifically in the context of data mining and analysis. Reinforcement learning, an inter…
Truly noiseless probabilistic amplification
Vedran Dunjko, Erika Andersson
Most of the schemes for "noiseless" amplification of coherent states, which have recently been attracting theoretical and experimental interest, share a common trait: the amplifica…
Computational speedups using small quantum devices
Vedran Dunjko, Yimin Ge, J. Ignacio Cirac
Suppose we have a small quantum computer with only M qubits. Can such a device genuinely speed up certain algorithms, even when the problem size is much larger than M? Here we answ…
Smooth input preparation for quantum and quantum-inspired machine learning
Zhikuan Zhao, Jack K. Fitzsimons, Patrick Rebentrost +2
Machine learning has recently emerged as a fruitful area for finding potential quantum computational advantage. Many of the quantum enhanced machine learning algorithms critically…
Neural Network Operations and Susuki-Trotter evolution of Neural Network States
Nahuel Freitas, Giovanna Morigi, Vedran Dunjko
It was recently proposed to leverage the representational power of artificial neural networks, in particular Restricted Boltzmann Machines, in order to model complex quantum states…