A coherence law based on the readout-visible aligned coherence rate (a Rayleigh quotient of the noise generator) predicts gradient survival in noisy U(1)-equivariant QNNs, with simulations confirming R²=0.979 and a special channel test showing no loss where predicted.
Limitations of optimization algorithms on noisy quantum devices
4 Pith papers cite this work, alongside 22 external citations. Polarity classification is still indexing.
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Gaussian randomized rounding on two-qubit marginals of depth-D circuits with local depolarizing noise p yields samples whose expected Max-Cut cost matches the noisy quantum device up to an approximation ratio of 1-O[(1-p)^D].
KPCA reduces QAOA parameters for Max-Cut on graphs, outperforming PCA at depths 4 and 8 while cutting circuit evaluations by over 93%.
Pauli Correlation Encoding framework achieves competitive or superior solutions on QOPTLib benchmark instances for combinatorial optimization.
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A Coherence Law for Trainability in Noisy Equivariant Quantum Neural Networks
A coherence law based on the readout-visible aligned coherence rate (a Rayleigh quotient of the noise generator) predicts gradient survival in noisy U(1)-equivariant QNNs, with simulations confirming R²=0.979 and a special channel test showing no loss where predicted.
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Sampling (noisy) quantum circuits through randomized rounding
Gaussian randomized rounding on two-qubit marginals of depth-D circuits with local depolarizing noise p yields samples whose expected Max-Cut cost matches the noisy quantum device up to an approximation ratio of 1-O[(1-p)^D].
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Dimensionality Reduction of QAOA Parameter Space with Kernel PCA for Max-Cut
KPCA reduces QAOA parameters for Max-Cut on graphs, outperforming PCA at depths 4 and 8 while cutting circuit evaluations by over 93%.
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Benchmark of Pauli Correlation Encoding for different optimisation problems
Pauli Correlation Encoding framework achieves competitive or superior solutions on QOPTLib benchmark instances for combinatorial optimization.