Robust multi-mode superconducting circuit optimized for quantum information processing

Multi-mode superconducting circuits offer a promising platform for engineering robust systems for quantum computation. Previous studies indicate that single-mode devices cannot be engineered to simultaneously exhibit resilience against multiple decoherence sources due to conflicting requirements. In contrast, multi-mode systems offer increased flexibility and have proven capable of overcoming these fundamental limitations. Here, we present a multi-mode device optimized for quantum information processing. It features an anharmonicity of a third of the qubit frequency and reduced energy dispersion caused by charge and magnetic flux fluctuations. It exhibits improvements over the fundamental errors limiting Transmon and Fluxonium coherence and control, achieving ratios between the total coherence time and the gate time $T_2/t_g$ one order of magnitude larger than Transmon and two times larger than Fluxonium for microwave charge drives, assuming equal dielectric and inductive loss quality factors and limited drive strength. It furthermore demonstrates robustness against fabrication errors, a major limitation in many proposed multi-mode devices.

💡 Research Summary

This paper presents a novel multi‑mode superconducting circuit, dubbed the “Fluxmon” qubit, that simultaneously addresses the long‑standing trade‑offs between anharmonicity, control strength, and noise resilience which limit single‑mode devices such as the Transmon and Fluxonium. The authors start from a generic four‑node, five‑branch lumped‑element network. Two inductors in series with a Josephson junction form a closed loop, and a second junction couples a third node, yielding three strongly interacting modes: one periodic charge mode and two extended modes. The Hamiltonian is expressed in terms of charge ( n̂ ) and flux ( φ̂ ) operators, with charging‑energy and inductive‑energy matrices derived from the capacitance and inductance networks. External charge bias (n_g^ext) and magnetic‑flux bias (φ_ext) are incorporated via an additional term ˆH_ext, allowing realistic modeling of environmental fluctuations.

To locate an operating point that maximizes performance, the authors employ an evolutionary‑algorithm based optimization framework previously developed in Ref.