Logical performance projections for quantum error correction in a hybrid cat-transmon architecture

Inspired by recent experimental demonstrations, we study the long-term prospects of a hybrid cat-transmon quantum computing architecture. In this architecture, dissipative cat qubits play the role of data qubits, and syndromes are measured using ancillary transmon qubits. The cat qubits' noise bias enables more hardware-efficient quantum error correction, while the use of transmons allows for high-fidelity and easy-to-implement syndrome measurements. We numerically benchmark the performance of bias-preserving cat-transmon entangling gates, then estimate the performance of surface code logical memories. We identify parameter regimes where this architecture can offer significant overhead reductions relative to architectures without biased noise. In particular, we find that the cat architecture has the potential to offer a significant advantage at relatively small cat mean photon number.