Provable learning separation for predicting time-evolution of quantum many-body systems
Given that quantum computers are naturally suited to simulate the behavior of quantum many-body systems, an immediate question arises: can one formulate physically motivated quantum machine learning (QML) tasks that exhibit learning separations? We address this problem by studying the learnability of quantum many-body dynamics from the perspective of probably approximately correct (PAC)-learning. Concretely, we devise a supervised learning problem where the training set consists of specifications of randomized stabilizer probe states, evolution times sampled uniformly from a polynomially large
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- LinkedLinked via arxiv author · 85%Rahul Bandyopadhyay →
“Provable learning separation for predicting time-evolution of quantum many-body systems”
- LinkedLinked via arxiv author · 85%Riccardo Molteni →
“Provable learning separation for predicting time-evolution of quantum many-body systems”
- LinkedLinked via arxiv author · 85%Jens Eisert →
“Provable learning separation for predicting time-evolution of quantum many-body systems”
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“Provable learning separation for predicting time-evolution of quantum many-body systems”
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“Provable learning separation for predicting time-evolution of quantum many-body systems”
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