4 papers
Provable learning separation for predicting time-evolution of quantum many-body systems
Rahul Bandyopadhyay, Riccardo Molteni, Jens Eisert +2
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 quantu…
Quantum machine learning advantages beyond hardness of evaluation
Riccardo Molteni, Simon C. Marshall, Vedran Dunjko
The most general examples of quantum learning advantages involve data labeled by cryptographic or intrinsically quantum functions, where classical learners are limited by the infea…
Testing the presence of balanced and bipartite components in a sparse graph is QMA1-hard
Massimiliano Incudini, Casper Gyurik, Riccardo Molteni +1
Determining whether an abstract simplicial complex, a discrete object often approximating a manifold, contains multi-dimensional holes is a task deeply connected to quantum mechani…
Limitations of measure-first protocols in quantum machine learning
Casper Gyurik, Riccardo Molteni, Vedran Dunjko
In recent works, much progress has been made with regards to so-called randomized measurement strategies, which include the famous methods of classical shadows and shadow tomograph…