Efficient Training of Layerwise-Commuting PQCs with Parallel Gradient Estimation

Training variational quantum algorithms is normally expensive because each step requires repeated measurements on the quantum device to find the gradient. There exists a shortcut to bypass this cost, but it only works for circuits that are too simple to be useful. By building up and training the circuit one layer at a time, we keep the power of more expressive circuits while still cutting the number of measurements needed.

Date: 15 September 2024
Journal/conference link and reference: 2024 IEEE International Conference on Quantum Computing and Engineering (QCE’24) https://ieeexplore.ieee.org/document/10821191