arXiv subjects

Anthony Walsh

Publications and source records attributed to Anthony Walsh.

3 recordsLinked to original sources

arXiv2026

Magnetic Force Imaging of 2D Topological Insulators

Two-dimensional topological insulators are central to our understanding of the connection between topological symmetries in a material and its band electronics. Within this class of materials, a breadth of complex quantum behaviors, such as persistent spin-polarized current states in the presence of a broken time reversal symmetry, and temperature-independent topological protection of quantum states, are thought to exist. However, current studies using photoemission and spectroscopic analyses or transport experiments fail to provide insight into the interplay between the physical 2D manifold and the band topology itself, since they do not provide spatial resolution of the phenomena to be understood. In this work, we develop a methodology for applying magnetic force microscopy to such systems to address this issue. Using well-characterized 2D crystallites of bismuth telluride ($Bi_2$$Te_3$), we image the magnetic signal directly associated with topological edge states. The observed phase contrast is remarkably robust at a temperature of 25{\deg}C and occurs across crystallite sizes and shapes. A detailed analysis of the magnetic imaging suggests that the current observed is composed of two parts: the first is a persistent current ($I_{Persistent}$) as predicted by theory, and the second is due to Faraday induction, $I_{Faraday}$. Damping dynamics of the cantilever during imaging further suggest that this Faraday EMF is established by spin accumulation along the 1D edge channel of the crystal, which then converts to a charge current in the presence of time reversal symmetry breaking, creating a novel form of rectification in the channel. This unexpected result can prompt new ideas for topology-based circuit elements with extremely low losses and power consumption.

cond-mat.mes-hall↗

arXiv2026

MerLin: A Discovery Engine for Photonic and Hybrid Quantum Machine Learning

Identifying where quantum models may offer practical benefits in near term quantum machine learning (QML) requires moving beyond isolated algorithmic proposals toward systematic and empirical exploration across models, datasets, and hardware constraints. We introduce MerLin, an open-source framework designed as a discovery engine for photonic and hybrid quantum machine learning. MerLin integrates optimized strong simulation of linear optical circuits into standard PyTorch and scikit learn workflows, enabling end-to-end differentiable training of quantum layers. MerLin is designed around systematic benchmarking and reproducibility. As an initial contribution, we reproduce eighteen state-of-the-art photonic and hybrid QML works spanning kernel methods, reservoir computing, convolutional and recurrent architectures, generative models, and modern training paradigms. These reproductions are released as reusable, modular experiments that can be directly extended and adapted, establishing a shared experimental baseline consistent with empirical benchmarking methodologies widely adopted in modern artificial intelligence. By embedding photonic quantum models within established machine learning ecosystems, MerLin allows practitioners to leverage existing tooling for ablation studies, cross-modality comparisons, and hybrid classical-quantum workflows. The framework already implements hardware-aware features, allowing tests on available quantum hardware while enabling exploration beyond its current capabilities, positioning MerLin as a forward-looking co-design tool linking algorithms, benchmarks, and hardware.

cs.LG↗

arXiv2025

Establishing Baselines for Photonic Quantum Machine Learning: Insights from an Open, Collaborative Initiative

The Perceval Challenge is an open, reproducible benchmark designed to assess the potential of photonic quantum computing for machine learning. Focusing on a reduced and hardware-feasible version of the MNIST digit classification task or near-term photonic processors, it offers a concrete framework to evaluate how photonic quantum circuits learn and generalize from limited data. Conducted over more than three months, the challenge attracted 64 teams worldwide in its first phase. After an initial selection, 11 finalist teams were granted access to GPU resources for large-scale simulation and photonic hardware execution through cloud service. The results establish the first unified baseline of photonic machine-learning performance, revealing complementary strengths between variational, hardware-native, and hybrid approaches. This challenge also underscores the importance of open, reproducible experimentation and interdisciplinary collaboration, highlighting how shared benchmarks can accelerate progress in quantum-enhanced learning. All implementations are publicly available in a single shared repository (https://github.com/Quandela/HybridAIQuantum-Challenge), supporting transparent benchmarking and cumulative research. Beyond this specific task, the Perceval Challenge illustrates how systematic, collaborative experimentation can map the current landscape of photonic quantum machine learning and pave the way toward hybrid, quantum-augmented AI workflows.

quant-ph↗