arXiv subjects

Paul Baity

Publications and source records attributed to Paul Baity.

5 recordsLinked to original sources

arXiv2026

Inverse mask design for interference lithography using automatic differentiable wave propagation

Interference lithography (IL) is powerful for fabricating high-resolution periodic nanostructures, but designing masks to produce non-periodic patterns remains challenging. We introduce a gradient-based optimization framework for binary IL mask design using automatic differentiation. The forward model is implemented using the differentiable angular spectrum method (ASM). The inverse mask design is formulated as an optimization problem, where the mask logits are updated through backpropagation of the loss between the simulated field amplitude and the target pattern. We optimize a mask that reproduces a target pattern with only 0.1% isolated pixel-level defects, resolving features at half the mask pixel pitch. To scale mask optimization, we employ the shifted ASM, which partitions the mask into patches that are propagated independently and summed at the image plane. For a 3.84 mm$\times$3.84 mm mask, shifted ASM with 16 patches reduces peak GPU memory by 3.8$\times$ at only 1.3$\times$ runtime cost relative to standard ASM. With gradient checkpointing, peak memory is reduced by 7.4$\times$ at 2$\times$ runtime. Distributing across multiple GPUs further accelerates the optimization. This work establishes a physics-informed, machine learning-driven approach for IL mask design, moving a step further towards complex, non-periodic patterns. The source code is available at https://github.com/chuntian236/holography-optimization.git .

physics.optics↗

arXiv2024

Enhancing Future Link Prediction in Quantum Computing Semantic Networks through LLM-Initiated Node Features

Quantum computing is rapidly evolving in both physics and computer science, offering the potential to solve complex problems and accelerate computational processes. The development of quantum chips necessitates understanding the correlations among diverse experimental conditions. Semantic networks built on scientific literature, representing meaningful relationships between concepts, have been used across various domains to identify knowledge gaps and novel concept combinations. Neural network-based approaches have shown promise in link prediction within these networks. This study proposes initializing node features using LLMs to enhance node representations for link prediction tasks in graph neural networks. LLMs can provide rich descriptions, reducing the need for manual feature creation and lowering costs. Our method, evaluated using various link prediction models on a quantum computing semantic network, demonstrated efficacy compared to traditional node embedding techniques.

cs.LG↗

arXiv2023

Quantum-centric Supercomputing for Materials Science: A Perspective on Challenges and Future Directions

Computational models are an essential tool for the design, characterization, and discovery of novel materials. Hard computational tasks in materials science stretch the limits of existing high-performance supercomputing centers, consuming much of their simulation, analysis, and data resources. Quantum computing, on the other hand, is an emerging technology with the potential to accelerate many of the computational tasks needed for materials science. In order to do that, the quantum technology must interact with conventional high-performance computing in several ways: approximate results validation, identification of hard problems, and synergies in quantum-centric supercomputing. In this paper, we provide a perspective on how quantum-centric supercomputing can help address critical computational problems in materials science, the challenges to face in order to solve representative use cases, and new suggested directions.

quant-ph↗

arXiv2023

Characterizing Niobium Nitride Superconducting Microwave Coplanar Waveguide Resonator Array for Circuit Quantum Electrodynamics in Extreme Conditions

The high critical magnetic field and relatively high critical temperature of niobium nitride (NbN) make it a promising material candidate for applications in superconducting quantum technology. However, NbN-based devices and circuits are sensitive to decoherence sources such as two-level system (TLS) defects. Here, we numerically and experimentally investigate NbN superconducting microwave coplanar waveguide resonator arrays, with a 100 nm thickness, capacitively coupled to a common coplanar waveguide on a silicon chip. We observe that the resonators' internal quality factor (Qi) decreases from Qi ~ 1.07*10^6 in a high power regime (< nph > = 27000) to Qi ~ 1.36 *10^5 in single photon regime at temperature T = 100 mK. Data from this study is consistent with the TLS theory, which describes the TLS interactions in resonator substrates and interfaces. Moreover, we study the temperature dependence internal quality factor and frequency tuning of the coplanar waveguide resonators to characterise the quasiparticle density of NbN. We observe that the increase in kinetic inductance at higher temperatures is the main reason for the frequency shift. Finally, we measure the resonators' resonance frequency and internal quality factor at single photon regime in response to in-plane magnetic fields B||. We verify that Qi stays well above 10^4 up to B|| = 240 mT in the photon number < nph > = 1.8 at T = 100 mK. Our results may pave the way for realising robust microwave superconducting circuits for circuit quantum electrodynamics (cQED) at high magnetic fields necessary for fault-tolerant quantum computing, and ultrasensitive quantum sensing.

quant-ph↗

arXiv2020

Coherent superconducting qubits from a subtractive junction fabrication process

Josephson tunnel junctions are the centerpiece of almost any superconducting electronic circuit, including qubits. Typically, the junctions for qubits are fabricated using shadow evaporation techniques to reduce dielectric loss contributions from the superconducting film interfaces. In recent years, however, sub-micron scale overlap junctions have started to attract attention. Compared to shadow mask techniques, neither an angle dependent deposition nor free-standing bridges or overlaps are needed, which are significant limitations for wafer-scale processing. This comes at the cost of breaking the vacuum during fabrication, but simplifies integration in multi-layered circuits, implementation of vastly different junction sizes, and enables fabrication on a larger scale in an industrially-standardized process. In this work, we demonstrate the feasibility of a subtractive process for fabrication of overlap junctions. In an array of test contacts, we find low aging of the average normal state resistance of only 1.6\% over 6 months. We evaluate the coherence properties of the junctions by employing them in superconducting transmon qubits. In time domain experiments, we find that both, the qubit life- and coherence time of our best device, are on average greater than $20\,\si{\micro\second}$. Finally, we discuss potential improvements to our technique. This work paves the way towards a more standardized process flow with advanced materials and growth processes, and constitutes an important step for large scale fabrication of superconducting quantum circuits.

quant-ph↗