50 citations · 83 across the 3 of their papers we have counts for
13 papers · 1 filter
Quantum Machine Learning For Classical Data
Leonard Wossnig
In this dissertation, we study the intersection of quantum computing and supervised machine learning algorithms, which means that we investigate quantum algorithms for supervised m…
Statistical Limits of Supervised Quantum Learning
Carlo Ciliberto, Andrea Rocchetto, Alessandro Rudi +1
Within the framework of statistical learning theory it is possible to bound the minimum number of samples required by a learner to reach a target accuracy. We show that if the boun…
Computation of molecular excited states on IBM quantum computers using a discriminative variational quantum eigensolver
Jules Tilly, Glenn Jones, Hongxiang Chen +2
Solving for molecular excited states remains one of the key challenges of modern quantum chemistry. Traditional methods are constrained by existing computational capabilities, limi…
Quantum State Discrimination Using Noisy Quantum Neural Networks
Andrew Patterson, Hongxiang Chen, Leonard Wossnig +3
Near-term quantum computers are noisy, and therefore must run algorithms with a low circuit depth and qubit count. Here we investigate how noise affects a quantum neural network (Q…
Cost function embedding and dataset encoding for machine learning with parameterized quantum circuits
Shuxiang Cao, Leonard Wossnig, Brian Vlastakis +2
Machine learning is seen as a promising application of quantum computation. For near-term noisy intermediate-scale quantum (NISQ) devices, parametrized quantum circuits (PQCs) have…
Dynamical mean field theory algorithm and experiment on quantum computers
I. Rungger, N. Fitzpatrick, H. Chen +12
The developments of quantum computing algorithms and experiments for atomic scale simulations have largely focused on quantum chemistry for molecules, while their application in co…