462 citations · 4.5k across the 108 of their papers we have counts for
113 papers
Quantifying the Value of Privileged Information Using a PAC-Bayesian Approach
Vasily Bokov, Sebastian Schmitt, Vedran Dunjko +1
In practice, various learning scenarios provide access to auxiliary features exclusively during training. Incorporating such data to enhance model performance gave rise to a paradi…
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…
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…
Characterizing Trainability of Instantaneous Quantum Polynomial Circuit Born Machines
Kevin Shen, Susanne Pielawa, Vedran Dunjko +1
Instantaneous quantum polynomial quantum circuit Born machines (IQP-QCBMs) have been proposed as quantum generative models with a classically tractable training objective based on…