Three-dimensional superconducting radio-frequency (SRF) cavities provide exceptionally long-lived electromagnetic modes and, when coupled to nonlinear elements such as transmon qubits,become promising architectures for bosonic quantum information processing. The inverse design of such systems, i.e., recovering device geometries that produce specified electromagnetic and coupling targets, is generally a one-to-many problem. The qubit-cavity coupling strength depends sensitively on both the transmon geometry and its position within the cavity’s electromagnetic field. As these systems scale up and their design parameter spaces grow, the cost of conventional iterative simulation becomes prohibitive. We present two deep neural network (DNN) approaches that address this inverse-design problem at complementary levels of the design stack. The first proposes SRF cavity geometries that produce target cavity observables. The second proposes transmon qubit designs that produce target qubit-cavity parameters — the coupling rate, qubit frequency, and anharmonicity (g,νq,α). The recovered candidate designs match the targets to within ∼5\% (cavity) and ∼2\% (transmon), confirmed by end-to-end re-simulation. Both approaches map desired device behavior directly to candidate designs, a fast alternative to the iterative simulation studies usually required.
Superconducting radio-frequency (SRF) cavities offer a promising platform for quantum computing due to their long coherence times and large accessible Hilbert spaces, yet integratingnonlinear elements like transmons for control often introduces additional loss. We report a multimode quantum system based on a 2-cell elliptical shaped SRF cavity, comprising two cavity modes weakly coupled to an ancillary transmon circuit, designed to preserve coherence while enabling efficient control of the cavity modes. We mitigate the detrimental effects of the transmon decoherence through careful design optimization that reduces transmon-cavity couplings and participation in the dielectric substrate and lossy interfaces, to achieve single-photon lifetimes of 20.6 ms and 15.6 ms for the two modes, and a pure dephasing time exceeding 40 ms. This marks an order-of-magnitude improvement over prior 3D multimode memories. Leveraging sideband interactions and novel error-resilient protocols, including measurement-based correction and post-selection, we achieve high-fidelity control over quantum states. This enables the preparation of Fock states up to N=20 with fidelities exceeding 95%, the highest reported to date to the authors‘ knowledge, as well as two-mode entanglement with coherence-limited fidelities reaching up to 99.9% after post-selection. These results establish our platform as a robust foundation for quantum information processing, allowing for future extensions to high-dimensional qudit encodings.
We propose a novel quantum transduction hybrid system based on the coupling of long-coherence time superconducting cavities with electro-optic resonators to achieve high-efficiencyand high-fidelity in quantum communication protocols and quantum sensing.
Due to their unique properties as lossless, nonlinear circuit elements, Josephson junctions lie at the heart of superconducting quantum information processing. Previously, we demonstrateda two-layer, submicrometer-scale overlap junction fabrication process suitable for qubits with long coherence times. Here, we extend the overlap junction fabrication process to micrometer-scale junctions. This allows us to fabricate other superconducting quantum devices. For example, we demonstrate an overlap-junction-based Josephson parametric amplifier that uses only 2 layers. This efficient fabrication process yields frequency-tunable devices with negligible insertion loss and a gain of ~ 30 dB. Compared to other processes, the overlap junction allows for fabrication with minimal infrastructure, high yield, and state-of-the-art device performance.