A single-layer complex-valued neural network constrained to the Stiefel manifold via Cayley updates is used to learn and transpile quantum circuit unitaries, with fidelity reaching 1 on reported toy examples.
In Proceedings of the 33rd International Conference on Machine Learning (ICML) , pages 1120–1128
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Quantum Circuit Design using Complex valued Neural Network in Stiefel Manifold
A single-layer complex-valued neural network constrained to the Stiefel manifold via Cayley updates is used to learn and transpile quantum circuit unitaries, with fidelity reaching 1 on reported toy examples.