arXiv subjects

Evan Williams

Publications and source records attributed to Evan Williams.

8 recordsLinked to original sources

arXiv2026

Engineering of Dual Wavelength, Polarization Selective Metalenses in Silicon Carbide

Spin defects in silicon carbide (SiC) are promising candidates for integrated quantum photonics, offering long-lived spin states and near-infrared emission suitable for low-loss photonic integration and fibre-based quantum communication. However, light extraction from these defects remains challenging due the relatively high refractive index of SiC. Metalenses offer a compact approach to enhance light collection by engineering the wavefront directly at the material interface. Here, we design and fabricate monolithic metalenses from SiC bulk material that simultaneously operate at 860 and 1240 nm, matching with emission from the nitrogen vacancy and silicon vacancy colour centers. By independently engineering the phase response at both wavelengths, the metalens enables collection and polarization manipulation of the emitted light. We further employ the metalenses to demonstrate optically detected magnetic resonance of both defects simultaneously. These multifunctional metalenses provide a compact optical interface for scalable integrated SiC photonic devices.

quant-ph↗

arXiv2026

Twist-Reconfigurable van der Waals Moir\'e Photonic Crystals

Moir\'e photonics has emerged as a fascinating concept to design and in situ control of the optical bands. Moir\'e enabled light localisation arises from the relative twist between periodic layers, rather than from fixed, pre-fabricated cavity features. So far, however, the realisation of practical moir\'e photonic crystals in the visible range has been elusive, due to challenges in engineering nanoscale structures and twisting them dynamically post fabrication. Here, we realise a mechanically reconfigurable moir\'e photonic crystal, comprising from two patterned van der Waals crystals (tungsten di sulphide, WS$_2$) slabs separated by an optically active hexagonal boron nitride (hBN) spacer. We reconfigured the same pair of WS$_2$ slabs from a twist angle of 3.8{\deg} to 8.4{\deg} and reconstructed their three-dimensional dispersion using momentum-resolved reflectivity spectroscopy. Further, by reducing the twist angle between the slabs, we observe a denser manifold of folded and hybridised resonances that coincides with a 30-fold enhancement of emission from embedded colour centres. Our results open exciting opportunities for in-situ dispersion engineering and programmable light matter interactions employing van der Waals nanostructures.

physics.optics↗

arXiv2026

Twist-Controlled Modulation of Quantum Emitters in a Van der Waals Bilayer

Stacking and twisting two dimensional materials has garnered enormous attention across the condensed matter and the nanophotonic communities. The surge of interest stems from the emergence of novel photophysical phenomena that arise due to the interlayer coupling of the individual layers. Here, we demonstrate that the twist degree of freedom can modulate a single quantum emitter at room temperature. We employ a van der Waals homobilayer of hexagonal boron nitride (hBN) and model the emission properties of quantum emitters as a function of the twist angle. Density functional theory results show that the embedded emitters are strongly influenced by the twist angle and the stacking of the top hBN layer. We consequently engineer these systems experimentally, and demonstrate in-situ tuning of embedded quantum emitters by mechanically twisting the top hBN layer, achieving tunability of over 30 nm (~ 100 meV). Our work demonstrates that mechanical twisting can be harnessed to modulate the embedded quantum emitters in a vdW material, marking a crucial step towards a programmable on-chip quantum circuitry.

cond-mat.mtrl-sci↗

arXiv2026

Deterministic integration of quantum emitters and optical cavities in a van der Waals crystal

Single-photon emitters in hexagonal boron nitride (hBN) combine bright optical emission with optically addressable spin states, offering a promising platform for integrated quantum photonics. However, their stochastic creation and spectral variability have prevented deterministic integration with photonic cavities. Here we demonstrate a fabrication protocol that enables precise, deterministic coupling of pre-selected visible emitters to circular Bragg grating (CBG) cavities in hBN. By patterning etched alignment markers and performing prefabrication confocal mapping, we locate emitters with sub-micron accuracy and design cavity geometries matched to their zero-phonon line wavelengths. The resulting devices show enhanced emission and reliable spectral alignment between emitter and cavity mode. This work establishes a deterministic cavity-emitter integration scheme in a van der Waals material and provides a scalable route towards on-chip quantum photonic and spin-based platforms using hBN.

physics.optics↗

arXiv2025

From PyTorch to Calyx: An Open-Source Compiler Toolchain for ML Accelerators

We present an end-to-end open-source compiler toolchain that targets synthesizable SystemVerilog from ML models written in PyTorch. Our toolchain leverages the accelerator design language Allo, the hardware intermediate representation (IR) Calyx, and the CIRCT project under LLVM. We also implement a set of compiler passes for memory partitioning, enabling effective parallelism in memory-intensive ML workloads. Experimental results demonstrate that our compiler can effectively generate optimized FPGA-implementable hardware designs that perform reasonably well against closed-source industry-grade tools such as Vitis HLS.

cs.AR↗

arXiv2025

Quantum Emitters in Flux Grown hBN

Hexagonal boron nitride (hBN) is an emerging material for use in quantum technologies, hosting bright and stable single photon emitters (SPEs). The B-center is one promising SPE in hBN, due to the near-deterministic creation methods and regular emission wavelength. However, incorporation of B-centers in high-quality crystals remains challenging, typically relying on additional post-growth methods to increase creation efficiency. Here, we have demonstrated controlled carbon doping of hBN during growth, using a metal flux based method to increase the efficiency of B-center creation. Importantly, single B-centers with $g^{(2)}(0) < 0.5$ were able to be generated in the as-grown hBN when carbon additions during growth exceeded 2.5 wt.% C. Resonant excitation measurements revealed linewidths of 3.5 GHz with only moderate spectral diffusion present, demonstrating the applicability of the as-grown hBN as a host for high quality B-centers.

cond-mat.mtrl-sci↗

arXiv2021

Accenture at CheckThat! 2021: Interesting claim identification and ranking with contextually sensitive lexical training data augmentation

This paper discusses the approach used by the Accenture Team for CLEF2021 CheckThat! Lab, Task 1, to identify whether a claim made in social media would be interesting to a wide audience and should be fact-checked. Twitter training and test data were provided in English, Arabic, Spanish, Turkish, and Bulgarian. Claims were to be classified (check-worthy/not check-worthy) and ranked in priority order for the fact-checker. Our method used deep neural network transformer models with contextually sensitive lexical augmentation applied on the supplied training datasets to create additional training samples. This augmentation approach improved the performance for all languages. Overall, our architecture and data augmentation pipeline produced the best submitted system for Arabic, and performance scales according to the quantity of provided training data for English, Spanish, Turkish, and Bulgarian. This paper investigates the deep neural network architectures for each language as well as the provided data to examine why the approach worked so effectively for Arabic, and discusses additional data augmentation measures that should could be useful to this problem.

cs.CL↗

arXiv2020

Accenture at CheckThat! 2020: If you say so: Post-hoc fact-checking of claims using transformer-based models

We introduce the strategies used by the Accenture Team for the CLEF2020 CheckThat! Lab, Task 1, on English and Arabic. This shared task evaluated whether a claim in social media text should be professionally fact checked. To a journalist, a statement presented as fact, which would be of interest to a large audience, requires professional fact-checking before dissemination. We utilized BERT and RoBERTa models to identify claims in social media text a professional fact-checker should review, and rank these in priority order for the fact-checker. For the English challenge, we fine-tuned a RoBERTa model and added an extra mean pooling layer and a dropout layer to enhance generalizability to unseen text. For the Arabic task, we fine-tuned Arabic-language BERT models and demonstrate the use of back-translation to amplify the minority class and balance the dataset. The work presented here was scored 1st place in the English track, and 1st, 2nd, 3rd, and 4th place in the Arabic track.

cs.CL↗