arXiv subjects

Patrick J. Walsh

Publications and source records attributed to Patrick J. Walsh.

3 recordsLinked to original sources

arXiv2026

Bootstrapping, autonomous testing, and initialization system for Si/Si$_x$Ge$_{1-x}$ multi-quantum-dot devices

Semiconductor quantum dot (QD) devices have become central to advancements in spin-based quantum computing. However, the increasing complexity of modern QD devices makes calibration and control -- particularly at elevated temperatures -- a bottleneck to progress, highlighting the need for robust and scalable autonomous solutions. A major hurdle arises from trapped charges within the oxide layers, which induce random offset voltage shifts on gate electrodes, with a standard deviation of approximately 83 mV of variation within state-of-the-art present-day devices. Efficient characterization and tuning of large arrays of QD qubits depend on choices of automated protocols. Here, we introduce a physically intuitive framework for a bootstrapping, autonomous testing, and initialization system (BATIS) designed to streamline QD device evaluation and calibration. BATIS navigates high-dimensional gate voltage spaces, automating essential steps such as leakage testing, formation of all current channels, and gate characterization in the presence of trapped charges. For forming the current channels, BATIS follows a non-standard approach that requires a single set of measurements regardless of the number of channels. Demonstrated at 1.3 K on a quad-QD Si/Si$_x$Ge$_{1-x}$ device, BATIS eliminates the need for deep cryogenic environments during initial device diagnostics, significantly enhancing scalability and reducing setup times. By requiring only minimal prior knowledge of the device architecture, BATIS represents a platform-agnostic solution, adaptable to various QD systems, which bridges a critical gap in QD autotuning.

cond-mat.mes-hall↗

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↗

arXiv2018

Effects of Microstructure Formation on the Stability of Vapor Deposited Glasses

Glasses formed by physical vapor deposition (PVD) are an interesting new class of materials, exhibiting properties thought to be equivalent to those of glasses aged for thousands of years. Exerting control over the structure and properties of PVD glasses formed with different types of glass-forming molecules is now an emerging challenge. In this work, we study coarse grained models of organic glass formers containing fluorocarbon tails of increasing length, corresponding to an increased tendency to form microstructures. We use simulated PVD to examine how the presence of the microphase separated domains in the supercooled liquid influences the ability to form stable glasses. This model suggests that increasing molecule tail length results in decreased thermodynamic and kinetic stability of the molecules in PVD films. The reduced stability is further linked to the reduced ability of these molecules to equilibrate at the free surface during PVD. We find that as the tail length is increased, the relaxation time near the surface of the supercooled equilibrium liquid films of these molecules are slowed and become essentially bulk-like, due to the segregation of the fluorocarbon tails to the free surface. Surface diffusion is also markedly reduced due to clustering of the molecules at the surface. Based on these results, we propose a trapping mechanism where tails are unable to move between local phase separated domains on the relevant deposition time scales.

cond-mat.soft↗