24 papers
Cautious optimism for deep parameterized quantum circuits
Marie Kempkes, Elies Gil-Fuster, Carlos Bravo-Prieto +5
A central challenge in quantum machine learning is understanding the scaling behavior of parameterized quantum circuits (PQCs). In particular, it remains unclear how their performa…
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…
Provable quantum speedups for computing persistence in topological data analysis
Casper Gyurik, Alexander Schmidhuber, Robbie King +2
Topological data analysis (TDA) aims to extract noise-robust features from a data set by examining the number and persistence of holes in its topology. We provide an efficient quan…
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…
Universality of Classically Trainable, Quantum-Deployed Boson-Sampling Generative Models
Andrii Kurkin, Ulysse Chabaud, Zoltán Kolarovszki +3
Recent work on the instantaneous quantum polynomial-time (IQP) quantum-circuit Born machine (QCBM) highlights a promising paradigm for generative modeling: train classically, deplo…
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…