68 citations · 84 across the 8 of their papers we have counts for
8 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…
Evidence of Quantum Machine Learning Advantage with Tens of Noisy Qubits
Onur Danaci, Yash J. Patel, Riccardo Molteni +3
Learning problems involving quantum data are natural candidates for demonstrating an advantage in quantum machine learning. Recent results indicate that, for certain tasks and unde…
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…
Exponential quantum advantages in learning quantum observables from classical data
Riccardo Molteni, Casper Gyurik, Vedran Dunjko
Quantum computers are believed to bring computational advantages in simulating quantum many body systems. However, recent works have shown that classical machine learning algorithm…
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…
Shadows of quantum machine learning
Sofiene Jerbi, Casper Gyurik, Simon C. Marshall +2
Quantum machine learning is often highlighted as one of the most promising practical applications for which quantum computers could provide a computational advantage. However, a ma…
Optimization of the Memory Reset Rate of a Quantum Echo-State Network for Time Sequential Tasks
Riccardo Molteni, Claudio Destri, Enrico Prati
Quantum reservoir computing is a class of quantum machine learning algorithms involving a reservoir of an echo state network based on a register of qubits, but the dependence of it…