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Found 7 papers, 3 papers with code
On the expressivity of embedding quantum kernels
After proving the universality of embedding quantum kernels for both shift-invariant and composition kernels, we identify the directions towards new, more exotic, and unexplored quantum kernel families, for which it still remains open whether they correspond to efficient embedding quantum kernels.
Potential and limitations of random Fourier features for dequantizing quantum machine learning
We build on these insights to make concrete suggestions for PQC architecture design, and to identify structures which are necessary for a regression problem to admit a potential quantum advantage via PQC based optimization.
Understanding quantum machine learning also requires rethinking generalization
In this work, through systematic randomization experiments, we show that traditional approaches to understanding generalization fail to explain the behavior of such quantum models.
Exploiting symmetry in variational quantum machine learning
The success of variational quantum learning models crucially depends on finding a suitable parametrization of the model that encodes an inductive bias relevant to the learning task.
Encoding-dependent generalization bounds for parametrized quantum circuits
However, none of these generalization bounds depend explicitly on how the classical input data is encoded into the PQC.
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.
Data re-uploading for a universal quantum classifier
A single qubit provides sufficient computational capabilities to construct a universal quantum classifier.
Quantum Physics
