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Found 7 papers, 3 papers with code
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.
Classical surrogates for quantum learning models
In this work, we introduce the concept of a classical surrogate, a classical model which can be efficiently obtained from a trained quantum learning model and reproduces its input-output relations.
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.
The effect of data encoding on the expressive power of variational quantum machine learning models
Quantum computers can be used for supervised learning by treating parametrised quantum circuits as models that map data inputs to predictions.
PennyLane: Automatic differentiation of hybrid quantum-classical computations
PennyLane's core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation.
