Learning the capabilities of quantum computers using physics-informed neural networks

The computational power of contemporary quantum processors is limited by hardware errors that cause computations to fail. In principle, each quantum processor's computational capabilities can be captured by a capability function. A capability function quantifies how well a processor can run each possible quantum circuit by mapping a circuit to the processor's success rate on that circuit, as quantified by, e.g., fidelity. However, capability functions are typically unknown and challenging to model. In this talk, I will present results on using purpose-built artificial neural networks to learn an approximation to a processor's capability function. These “physics-informed” neural networks efficiently encode how errors propagate through and interfere within circuits, enabling accurate capability predictions even in the presence of strongly coherent errors.



SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.