Learning to rank quantum circuits for hardware-optimized performance enhancement

arXiv:2404.06535v1 Announce Type: cross
Abstract: We introduce and experimentally test a machine-learning-based method for ranking logically equivalent quantum circuits based on expected performance estimates derived from a training procedure conducted on real hardware. We apply our method to the problem of layout selection, in which abstracted qubits are assigned to physical qubits on a given device. Circuit measurements performed on IBM hardware indicate that the maximum and median fidelities of logically equivalent layouts can differ by an order of magnitude. …

abstract apply arxiv circuits cs.lg hardware machine performance quant-ph quantum qubits ranking test training type