11 citations · 14 across the 6 of their papers we have counts for
6 papers
Quantum Convolutional Autoencoders for Reconstruction-Based Anomaly Detection
Donovan Slabbert, Francesco Petruccione
Quantum convolutional neural networks (QCNNs) have become increasingly popular in quantum machine learning (QML) due to their efficient parameterization and hierarchical representa…
Hybrid Quantum-Classical Logistic Regression for Calibrated Classification of Pulsar Candidates
Chanelle Matadah Manfouo, Donovan Slabbert, Prince Koree Osei +1
Reliable pulsar candidate ranking requires probability estimates that are not only discriminative but also well calibrated. We evaluate hybrid quantum-calssical logistic regression…
Quantum kernel and HHL-based support vector machines for multi-class classification
Gabriela Pinheiro, Donovan Slabbert, Luis Kowada +1
We compare two quantum approaches that use support vector machines for multi-class classification on a reduced Sloan Digital Sky Survey (SDSS) dataset: the quantum kernel-based QSV…
Quantum Spectral Clustering: Comparing Parameterized and Neuromorphic Quantum Kernels
Donovan Slabbert, Dean Brand, Francesco Petruccione
We compare a parameterized quantum kernel (pQK) with a quantum leaky integrate-and-fire (QLIF) neuromorphic computing approach that employs either the Victor-Purpura or van Rossum…
Hybrid Quantum-Classical Feature Extraction approach for Image Classification using Autoencoders and Quantum SVMs
Donovan Slabbert, Francesco Petruccione
In order to leverage quantum computers for machine learning tasks such as image classification, careful consideration is required: NISQ-era quantum computers have limitations, whic…
Pulsar Classification: Comparing Quantum Convolutional Neural Networks and Quantum Support Vector Machines
Donovan Slabbert, Matt Lourens, Francesco Petruccione
Well-known quantum machine learning techniques, namely quantum kernel assisted support vector machines (QSVMs) and quantum convolutional neural networks (QCNNs), are applied to the…