[HTML][HTML] Qubit-efficient encoding schemes for binary optimisation problems
We propose and analyze a set of variational quantum algorithms for solving quadratic
unconstrained binary optimization problems where a problem consisting of $ n_c $ classical …
unconstrained binary optimization problems where a problem consisting of $ n_c $ classical …
Application of quantum-inspired generative models to small molecular datasets
Quantum and quantum-inspired machine learning has emerged as a promising and
challenging research field due to the increased popularity of quantum computing, especially …
challenging research field due to the increased popularity of quantum computing, especially …
Expressivity of parameterized quantum circuits for generative modeling of continuous multivariate distributions
Parameterized quantum circuits have been extensively used as the basis for machine
learning models in regression, classification, and generative tasks. For supervised learning …
learning models in regression, classification, and generative tasks. For supervised learning …
A continuous variable Born machine
Generative modelling has become a promising use case for near-term quantum computers.
Due to the fundamentally probabilistic nature of quantum mechanics, quantum computers …
Due to the fundamentally probabilistic nature of quantum mechanics, quantum computers …
Buildung Continuous Quantum-Classical Bayesian Neural Networks for a Classical Clinical Dataset
A Sakhnenko, J Sikora, J Lorenz - Proceedings of Recent Advances in …, 2024 - dl.acm.org
In this work, we are introducing a Quantum-Classical Bayesian Neural Network (QCBNN)
that is capable to perform uncertainty-aware classification of classical medical dataset. This …
that is capable to perform uncertainty-aware classification of classical medical dataset. This …
Studying the Impact of Quantum-Specific Hyperparameters on Hybrid Quantum-Classical Neural Networks
K Zaman, T Ahmed, M Kashif, MA Hanif… - arXiv preprint arXiv …, 2024 - arxiv.org
In current noisy intermediate-scale quantum devices, hybrid quantum-classical neural
networks (HQNNs) represent a promising solution that combines the strengths of classical …
networks (HQNNs) represent a promising solution that combines the strengths of classical …
A Hybrid Quantum-Classical Framework for Reinforcement Learning of Atari Games
D Freinberger - 2024 - repositum.tuwien.at
Quantum machine learning (QML) is a promising area of application for near-term quantum
computing devices, with hybrid quantum-classical models based on parameterized quantum …
computing devices, with hybrid quantum-classical models based on parameterized quantum …