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Found 4 papers, 2 papers with code
Single-component gradient rules for variational quantum algorithms
A popular set of optimization methods work on the estimate of the gradient, obtained by means of circuit evaluations.
Training Quantum Embedding Kernels on Near-Term Quantum Computers
Quantum embedding kernels (QEKs) constructed by embedding data into the Hilbert space of a quantum computer are a particular quantum kernel technique that allows to gather insights into learning problems and that are particularly suitable for noisy intermediate-scale quantum devices.
Evaluation of Parameterized Quantum Circuits: on the relation between classification accuracy, expressibility and entangling capability
Quantum Machine Learning, and Parameterized Quantum Circuits in a hybrid quantum-classical setup in particular, could bring advancements in accuracy by utilizing the high dimensionality of the Hilbert space as feature space.
Integration and Evaluation of Quantum Accelerators for Data-Driven User Functions
In this work we propose a system architecture for the integration of quantum accelerators.
