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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.
Quantum Kernel Alignment with Stochastic Gradient Descent
Specifically, we demonstrate that Pegasos is particularly effective for non-stationary data, which is an important challenge in real-world applications.
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