Hyperparameter importance and optimization of quantum neural networks across small datasets
As restricted quantum computers become available, research focuses on finding meaningful
applications. For example, in quantum machine learning, a special type of quantum circuit …
applications. For example, in quantum machine learning, a special type of quantum circuit …
Backpropagation scaling in parameterised quantum circuits
The discovery of the backpropagation algorithm ranks among one of the most important
moments in the history of machine learning, and has made possible the training of large …
moments in the history of machine learning, and has made possible the training of large …
End-to-end protocol for high-quality QAOA parameters with few shots
The quantum approximate optimization algorithm (QAOA) is a quantum heuristic for
combinatorial optimization that has been demonstrated to scale better than state-of-the-art …
combinatorial optimization that has been demonstrated to scale better than state-of-the-art …
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 …
SantaQlaus: A resource-efficient method to leverage quantum shot-noise for optimization of variational quantum algorithms
We introduce SantaQlaus, a resource-efficient optimization algorithm tailored for variational
quantum algorithms (VQAs), including applications in the variational quantum eigensolver …
quantum algorithms (VQAs), including applications in the variational quantum eigensolver …
Latency-aware adaptive shot allocation for run-time efficient variational quantum algorithms
K Ito - arXiv preprint arXiv:2302.04422, 2023 - arxiv.org
Efficient classical optimizers are crucial in practical implementations of Variational Quantum
Algorithms (VQAs). In particular, to make Stochastic Gradient Descent (SGD) resource …
Algorithms (VQAs). In particular, to make Stochastic Gradient Descent (SGD) resource …
Grover's Implementation of Quantum Binary Neural Networks
B Wrighter, SL Alarcon - 2023 IEEE International Conference …, 2023 - ieeexplore.ieee.org
Binary Neural Networks (BNNs) are the result of a simplification of network parameters in
Artificial Neural Networks (ANNs). The computational complexity of training ANNs increases …
Artificial Neural Networks (ANNs). The computational complexity of training ANNs increases …
[PDF][PDF] Algorithm selection and configuration for Nois Intermediate Scale Quantum methods for industrial applications
C Moussa - 2023 - scholarlypublications …
1.1 Background The field of quantum computing has gotten increased attention as a different
paradigm than classical computing for solving complex problems [142]. Quantum computers …
paradigm than classical computing for solving complex problems [142]. Quantum computers …