Graph Neural Networks for Parameterized Quantum Circuits Expressibility Estimation

arXiv:2405.08100v1 Announce Type: cross
Abstract: Parameterized quantum circuits (PQCs) are fundamental to quantum machine learning (QML), quantum optimization, and variational quantum algorithms (VQAs). The expressibility of PQCs is a measure that determines their capability to harness the full potential of the quantum state space. It is thus a crucial guidepost to know when selecting a particular PQC ansatz. However, the existing technique for expressibility computation through statistical estimation requires a large number of samples, which poses significant challenges due to …

abstract algorithms arxiv capability circuits cs.lg fundamental graph graph neural networks harness machine machine learning networks neural networks optimization qml quant-ph quantum space state type