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Found 5 papers, 1 papers with code
Exponential concentration in quantum kernel methods
Lastly, we show that when dealing with classical data, training a parametrized data embedding with a kernel alignment method is also susceptible to exponential concentration.
Subtleties in the trainability of quantum machine learning models
In this work we bridge the two frameworks and show that gradient scaling results for VQAs can also be applied to study the gradient scaling of QML models.
Can Error Mitigation Improve Trainability of Noisy Variational Quantum Algorithms?
On the other hand, our positive results for CDR highlight the possibility of engineering error mitigation methods to improve trainability.
Absence of Barren Plateaus in Quantum Convolutional Neural Networks
To derive our results we introduce a novel graph-based method to analyze expectation values over Haar-distributed unitaries, which will likely be useful in other contexts.
Noise-Induced Barren Plateaus in Variational Quantum Algorithms
Specifically, for the local Pauli noise considered, we prove that the gradient vanishes exponentially in the number of qubits $n$ if the depth of the ansatz grows linearly with $n$.
