199 citations · 711 across the 37 of their papers we have counts for
9 papers · 2 filters
Modelling Non-Markovian Quantum Processes with Recurrent Neural Networks
Leonardo Banchi, Edward Grant, Andrea Rocchetto +1
Quantum systems interacting with an unknown environment are notoriously difficult to model, especially in presence of non-Markovian and non-perturbative effects. Here we introduce…
Unitary equivalence between the Green's function and Schrödinger approaches for quantum graphs
Fabiano M. Andrade, Simone Severini
In a previous work [Andrade \textit{et al.}, Phys. Rep. \textbf{647}, 1 (2016)], it was shown that the exact Green's function (GF) for an arbitrarily large (although finite) quantu…
Adversarial quantum circuit learning for pure state approximation
Marcello Benedetti, Edward Grant, Leonard Wossnig +1
Adversarial learning is one of the most successful approaches to modelling high-dimensional probability distributions from data. The quantum computing community has recently begun…
Universal discriminative quantum neural networks
Hongxiang Chen, Leonard Wossnig, Simone Severini +2
Quantum mechanics fundamentally forbids deterministic discrimination of quantum states and processes. However, the ability to optimally distinguish various classes of quantum data…
Quantum Walk Search on Kronecker Graphs
Thomas G. Wong, Konstantin Wünscher, Joshua Lockhart +1
Kronecker graphs, obtained by repeatedly performing the Kronecker product of the adjacency matrix of an "initiator" graph with itself, have risen in popularity in network science d…
Hierarchical quantum classifiers
Edward Grant, Marcello Benedetti, Shuxiang Cao +5
Quantum circuits with hierarchical structure have been used to perform binary classification of classical data encoded in a quantum state. We demonstrate that more expressive circu…