Valeria Díaz Moreno | Searcharxiv

arXiv2025

CNOT gates in inductively coupled multi-fluxonium systems

High-fidelity two-qubit gates have been demonstrated in systems of two fluxonium qubits; however, the realization of scalable quantum processors requires maintaining low error rates in substantially larger architectures. In this work, we analyze a system of four inductively coupled fluxonium qubits to determine the impact of spectator qubits on the performance of a \textsc{cnot} gate. Our results show that spectator-induced errors are strongly suppressed when the transition frequencies of the spectator qubits are sufficiently detuned from those of the active qubits. We identify favorable frequency configurations for the four-qubit chain that yield \textsc{cnot} gate errors below $10^{-4}$ for gate times shorter than 100 ns. Leveraging the locality of the nearest-neighbor coupling, we extrapolate our findings to longer fluxonium chains, suggesting a viable path toward scalable, low-error quantum information processing.

quant-ph↗

arXiv2025

Benchmarking machine learning models for multi-class state recognition in double quantum dot data

Semiconductor quantum dots (QDs) are a leading platform for scalable quantum processors. However, scaling to large arrays requires reliable, automated tuning strategies for devices' bootstrapping, calibration, and operation, with many tuning aspects depending on accurately identifying QD device states from charge-stability diagrams (CSDs). In this work, we present a comprehensive benchmarking study of four modern machine learning (ML) architectures for multi-class state recognition in double-QD CSDs. We evaluate their performance across different data budgets and normalization schemes using both synthetic and experimental data. We find that the more resource-intensive models -- U-Nets and visual transformers (ViTs) -- achieve the highest MSE score (defined as $1-\mathrm{MSE}$) on synthetic data (over $0.98$) but fail to generalize to experimental data. MDNs are the most computationally efficient and exhibit highly stable training, but with substantially lower peak performance. CNNs offer the most favorable trade-off on experimental CSDs, achieving strong accuracy with two orders of magnitude fewer parameters than the U-Nets and ViTs. Normalization plays a nontrivial role: min-max scaling generally yields higher MSE scores but less stable convergence, whereas z-score normalization produces more predictable training dynamics but at reduced accuracy for most models. Overall, our study shows that CNNs with min-max normalization are a practical approach for QD CSDs.

cs.CV↗