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20182021
most citedTensor network approaches for learning non-linear dynamical laws

11 citations · 15 across the 4 of their papers we have counts for

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Showing 2019Show all

5 papers · 1 filter

quant-ph2019

Quantum certification and benchmarking

J. Eisert, D. Hangleiter, N. Walk +5

Concomitant with the rapid development of quantum technologies, challenging demands arise concerning the certification and characterization of devices. The promises of the field ca…

quant-ph2019

Stochastic gradient descent for hybrid quantum-classical optimization

Ryan Sweke, Frederik Wilde, Johannes Meyer +4

Within the context of hybrid quantum-classical optimization, gradient descent based optimizers typically require the evaluation of expectation values with respect to the outcome of…

quant-ph2019

Closing gaps of a quantum advantage with short-time Hamiltonian dynamics

Jonas Haferkamp, Dominik Hangleiter, Adam Bouland +3

Demonstrating a quantum computational speedup is a crucial milestone for near-term quantum technology. Recently, quantum simulation architectures have been proposed that have the p…

cs.LG2019

Expressive power of tensor-network factorizations for probabilistic modeling, with applications from hidden Markov models to quantum machine learning

Ivan Glasser, Ryan Sweke, Nicola Pancotti +2

Tensor-network techniques have enjoyed outstanding success in physics, and have recently attracted attention in machine learning, both as a tool for the formulation of new learning…

quant-ph2019

Lieb-Robinson bounds for open quantum systems with long-ranged interactions

Ryan Sweke, Jens Eisert, Michael Kastner

We state and prove four types of Lieb-Robinson bounds valid for many-body open quantum systems with power law decaying interactions undergoing out of equilibrium dynamics. We also…