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Found 5 papers, 3 papers with code
Equivariant quantum circuits for learning on weighted graphs
When training a parametrized quantum circuit in this setting to solve a specific problem, the choice of ansatz is one of the most important factors that determines the trainability and performance of the algorithm.
Hybrid quantum ResNet for car classification and its hyperparameter optimization
We test our approaches in a car image classification task and demonstrate a full-scale implementation of the hybrid quantum ResNet model with the tensor train hyperparameter optimization.
Layerwise learning for quantum neural networks
In order to ameliorate some of these challenges, we investigate a layerwise learning strategy for parametrized quantum circuits.
Quantum Approximate Optimization of Non-Planar Graph Problems on a Planar Superconducting Processor
For problems defined on our hardware graph we obtain an approximation ratio that is independent of problem size and observe, for the first time, that performance increases with circuit depth.
Quantum Physics
TensorFlow Quantum: A Software Framework for Quantum Machine Learning
We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data.
