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20182021
most citedLearnability of the output distributions of local quantum circuits

4 citations · 4 across the 1 of their papers we have counts for

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quant-ph20214 cited

Learnability of the output distributions of local quantum circuits

Marcel Hinsche, Marios Ioannou, Alexander Nietner +6

There is currently a large interest in understanding the potential advantages quantum devices can offer for probabilistic modelling. In this work we investigate, within two differe…

quant-ph2020

On the Quantum versus Classical Learnability of Discrete Distributions

Ryan Sweke, Jean-Pierre Seifert, Dominik Hangleiter +1

Here we study the comparative power of classical and quantum learners for generative modelling within the Probably Approximately Correct (PAC) framework. More specifically we consi…

quant-ph2020

Pinned QMA: The power of fixing a few qubits in proofs

Daniel Nagaj, Dominik Hangleiter, Jens Eisert +1

What could happen if we pinned a single qubit of a system and fixed it in a particular state? First, we show that this can greatly increase the complexity of static questions -- gr…

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

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…

quant-ph2019

Easing the Monte Carlo sign problem

Dominik Hangleiter, Ingo Roth, Daniel Nagaj +1

Quantum Monte Carlo (QMC) methods are the gold standard for studying equilibrium properties of quantum many-body systems -- their phase transitions, ground and thermal state proper…