High-fidelity Quantum Readout Processing via an Embedded SNAIL Amplifier

Abstract

Scalable, high-fidelity quantum-state readout remains a central challenge in the development of large-scale superconducting quantum processors. Conventional dispersive readout architectures depend on bulky isolators and external amplifiers, introducing significant hardware overhead and limiting opportunities for on-chip information processing. In this work, we propose a novel approach that embeds a nonlinear Superconducting Nonlinear Asymmetric Inductive eLement (SNAIL) into the readout chain, enabling coherent and directional processing of readout signals directly on-chip. This embedded SNAIL platform allows frequency-multiplexed resonators to interact through engineered couplings, forming a tunable readout-amplifier-output architecture that can manipulate quantum readout data \textit{in situ}. Through theoretical modeling and numerical optimization, we show that this platform enhances fidelity, suppresses measurement-induced decoherence, and simplifies hardware complexity. These results establish the hybridized SNAIL as a promising building block for scalable and coherent quantum-state readout in next-generation processors.

Figures (16)

• Figure 1: A circuit schematic of the embedded amplifiers platform. A qubit-resonator system is coupled to a nonlinear SNAIL resonator, which coupled to a fast output resonator. The nonlinearity of the SNAIL enables frequency multiplexed interactions between the resonators.
• Figure 2: (top) A mode connectivity schematic and physical couplings (denoted by the grey arrows). (bottom) Spectrum of the modes. The Lorentzians correspond to the resonator spectral responses, with colors matching their corresponding modes. The colored arrows represent pumps corresponding to different parametric processes, with $\Delta_{ij} = |\omega_i - \omega_j|$.
• Figure 3: LDA finds an optimal projection axis that maximizes the separation between the two states, in units of the projected noise. The highlighted region represent probability of misclassifications $P_{err}$.
• Figure 4: Top: The different parametric interactions can be modeled as a set of bosonic gates, modifying the IQ distributions of the fields. Bottom: The IQ distributions generated by applying the different gates (not to scale). Empty spaces indicate negligible population.
• Figure 5: (top) The photon occupancies in the readout (yellow), SNAIL (purple) and output (red) throughout the readout sequence. (bottom) The SNR (or Fisher Discriminant) between the qubit state. The colored overlays indicate the pulses applied.