activity
20022026
most citedQuantum machine learning: a classical perspective

516 citations · 4.2k across the 120 of their papers we have counts for

collaborators

127 papers

math.CO2026

The minimum of the graph likelihood

Simone Severini, Eric W. Weisstein

The likelihood of a finite simple undirected graph on vertices is the probability that the uniform sequential attachment process, which at each step joins a new vertex to a…

econ.GN2021★ 1 cited

The role of Common Agricultural Policy (CAP) in enhancing and stabilising farm income: an analysis of income transfer efficiency and the Income Stabilisation Tool

Luigi Biagini, Simone Severini

Since its inception, the E.U.'s Common Agricultural Policy (CAP) aimed at ensuring an adequate and stable farm income. While recognizing that the CAP pursues a larger set of object…

econ.GN2020★ 2 cited

The direct and indirect effect of CAP support on farm income enhancement:a farm-based econometric analysis

Simone Severini, Luigi Biagini

We assess the correlation between CAP support provided to farmers and their income and use of capital and labour in the first year of the new CAP regime. This is done applying thre…

quant-ph2019★ 26 cited

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…

cs.CV2018★ 7 cited

Graph Cut Segmentation Methods Revisited with a Quantum Algorithm

Lisa Tse, Peter Mountney, Paul Klein +1

The design and performance of computer vision algorithms are greatly influenced by the hardware on which they are implemented. CPUs, multi-core CPUs, FPGAs and GPUs have inspired n…

quant-ph2018★ 72 cited

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…