50 citations · 83 across the 4 of their papers we have counts for
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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…
Generative training of quantum Boltzmann machines with hidden units
Nathan Wiebe, Leonard Wossnig
In this article we provide a method for fully quantum generative training of quantum Boltzmann machines with both visible and hidden units while using quantum relative entropy as a…
An initialization strategy for addressing barren plateaus in parametrized quantum circuits
Edward Grant, Leonard Wossnig, Mateusz Ostaszewski +1
Parametrized quantum circuits initialized with random initial parameter values are characterized by barren plateaus where the gradient becomes exponentially small in the number of…