Expressibility and Entangling Capability of Parameterized Quantum Circuits for Hybrid Quantum‐Classical Algorithms

TLDR

Parameterized quantum circuits are essential for variational quantum algorithms, yet choosing circuits that are both expressive and shallow with few parameters is difficult. The study introduces expressibility and entangling‑capability descriptors to identify compact, highly expressive PQC fragments. Classical simulations compute these descriptors for a range of circuit topologies and gate sets, varying qubit connectivity and gate selection. Results show that ring or all‑to‑all two‑qubit connectivity and CX‑rotation sequences outperform line connectivity and CZ‑rotations, and that expressibility saturates with depth at rates and values that distinguish PQCs, providing useful guidance for algorithm design.

Abstract

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.