English

System-Level Design of Scalable Fluxonium Quantum Processors with Double-Transmon Couplers

Quantum Physics 2026-04-30 v1

Abstract

Fluxonium qubits combine long coherence times with strong anharmonicity, making them a promising platform for scalable superconducting quantum processors. Recent experiments have demonstrated high-fidelity operations in multi-qubit processors while suppressing stray qubit interactions using fluxonium-transmon-fluxonium (FTF) architectures. However, scaling such systems to larger arrays is constrained by a trade-off between achievable coupling strength, crosstalk suppression and qubit-qubit spacing required for wiring in a two-dimensional architecture. Multimode couplers, such as the double-transmon coupler (DTC), provide a promising pathway to overcome this limitation by enabling stronger interactions without compromising qubit spacing and isolation. Here, we develop a quantitative design framework for fluxonium-based quantum processors employing DTCs. Central to this work is a frequency-partitioned architecture that places qubit transitions, tunable-coupler excitations, and resonator modes in well-separated spectral regions. This structured allocation reduces parameter interdependence and enables the concurrent optimization of gate operations, readout, and qubit reset. By formulating device design as a multi-objective optimization problem under realistic experimental constraints and fabrication-induced disorder, we develop a tractable sequential workflow and determine a feasible parameter regime that simultaneously supports high-fidelity single- and two-qubit gates, fast qubit reset, and robust dispersive readout. These results establish a system-level architectural methodology that links circuit parameters to processor-level performance, and provide an experimentally actionable pathway toward scalable fluxonium quantum processors.

Keywords

Cite

@article{arxiv.2604.26373,
  title  = {System-Level Design of Scalable Fluxonium Quantum Processors with Double-Transmon Couplers},
  author = {Guo Xuan Chan and Wangwei Lan and Tenghui Wang and Xizheng Ma and Chunqing Deng and Lijing Jin},
  journal= {arXiv preprint arXiv:2604.26373},
  year   = {2026}
}

Comments

26 Pages, 13 Figures