Open AccessPhysicsComputer Science

Sukin Sim, Peter D. Johnson, Alan Aspuru-Guzik

2019.5.26Advanced Quantum Technologies

DOI: 10.1002/qute.201900070

tlooto Summary

It is investigated how expressibility “saturates” with increased circuit depth, finding that the rate and saturated value appear to be distinguishing features of a PQC, and methods and results can be useful for algorithm development and design of experiments.

Abstract

Parameterized quantum circuits (PQCs) play an essential role in the performance of many variational quantum algorithms. One challenge in implementing such algorithms is choosing an effective circuit that well represents the solution space while maintaining a low circuit depth and parameter count. To characterize and identify expressible, yet compact, circuits, several descriptors are proposed, including expressibility and entangling capability, that are statistically estimated from classical simulations. These descriptors are computed for different circuit structures, varying the qubit connectivity and selection of gates. From these simulations, circuit fragments that perform well with respect to the descriptors are identified. In particular, a substantial improvement in performance of two‐qubit gates in a ring or all‐to‐all connected arrangement, compared to that of those on a line, is observed. Furthermore, improvement in both descriptors is achieved by sequences of controlled X‐rotation gates compared to sequences of controlled Z‐rotation gates. In addition, it is investigated how expressibility “saturates” with increased circuit depth, finding that the rate and saturated value appear to be distinguishing features of a PQC. While the correlation between each descriptor and algorithm performance remains to be investigated, methods and results from this study can be useful for algorithm development and design of experiments.

Citation format

SIM, Sukin; JOHNSON, Peter D.; ASPURU-GUZIK, Alan. Expressibility and entangling capability of parameterized quantum circuits for hybrid quantum-classical algorithms [preprint]. arXiv, 2019. arXiv:1905.10876.