arXiv subjects

Andrew Ramsay

Publications and source records attributed to Andrew Ramsay.

8 recordsLinked to original sources

arXiv2026

Valley enhanced Rabi frequency in n-type planar Silicon-MOS quantum dot

Electron spin resonance spectroscopy (ESR) of a single electron in planar Si-MOS quantum dot is reported in the vicinity of a valley level anti-crossing. A number of one and two-photon resonances are observed due to mixing of magnetic spin-flip and electric valley-flip transitions. This allows the reconstruction of the energy-level diagram of a four state system with two valley and two spin states. Near the anti-crossing, an enhancement of the Rabi frequency is observed. This is attributed to an electric-dipole transition activated by admixing of the upper energy level due to inter-valley spin coupling. The electric-dipole transition may be driven via capacitive coupling between the ESR antenna, and the confinement gate. To characterize spin-valley coupling responsible for the enhancement, we measure the anisotropy of the g-factor difference between the two valley states, the mean g-factor and the inter-valley spin coupling for both in and out-of-plane magnetic fields. The inter-valley spin coupling is strongly modulated by the direction of the B-field, and is strongest for out-of-plane B-field, consistent with an in-plane spin-valley field. In principle, this strong Electric dipole spin resonance (EDSR) effect could be utilized for fast all-electrical spin control in small-scale devices.

cond-mat.mes-hall↗

arXiv2025

High Fidelity Qubit Control in a Natural Si-MOS Quantum Dot using a 300 mm Silicon on Insulator Wafer

We demonstrate high-fidelity single qubit control in a natural Si-MOS quantum dot fabricated in an industrial 300 mm wafer process on a silicon on insulator (SOI) wafer using electron spin resonance. A relatively high optimal Rabi frequency of 5 MHz is achieved, dynamically decoupling the electron spin from its 29-Si environment. Tracking the qubit frequency reduces the impact of low frequency noise in the qubit frequency and improves the $T^{Rabi}$ from 7 to 11 $\mu$s at a Rabi frequency of 5 MHz, resulting in Q-factors exceeding 50. Randomized benchmarking returns an average single gate control fidelity of 99.5 $\pm$ 0.3%. As a result of pulse-area calibration, this fidelity is limited by the Rabi Q-factor. These results show that a fast Rabi frequency, low charge noise, and a feedback protocol enable high fidelity in these Si-MOS devices, despite the low-frequency magnetic noise.

cond-mat.mes-hall↗

arXiv2024

Doing Personal LAPS: LLM-Augmented Dialogue Construction for Personalized Multi-Session Conversational Search

The future of conversational agents will provide users with personalized information responses. However, a significant challenge in developing models is the lack of large-scale dialogue datasets that span multiple sessions and reflect real-world user preferences. Previous approaches rely on experts in a wizard-of-oz setup that is difficult to scale, particularly for personalized tasks. Our method, LAPS, addresses this by using large language models (LLMs) to guide a single human worker in generating personalized dialogues. This method has proven to speed up the creation process and improve quality. LAPS can collect large-scale, human-written, multi-session, and multi-domain conversations, including extracting user preferences. When compared to existing datasets, LAPS-produced conversations are as natural and diverse as expert-created ones, which stays in contrast with fully synthetic methods. The collected dataset is suited to train preference extraction and personalized response generation. Our results show that responses generated explicitly using extracted preferences better match user's actual preferences, highlighting the value of using extracted preferences over simple dialogue history. Overall, LAPS introduces a new method to leverage LLMs to create realistic personalized conversational data more efficiently and effectively than previous methods.

cs.IR↗

arXiv2024

Open Assistant Toolkit -- version 2

We present the second version of the Open Assistant Toolkit (OAT-v2), an open-source task-oriented conversational system for composing generative neural models. OAT-v2 is a scalable and flexible assistant platform supporting multiple domains and modalities of user interaction. It splits processing a user utterance into modular system components, including submodules such as action code generation, multimodal content retrieval, and knowledge-augmented response generation. Developed over multiple years of the Alexa TaskBot challenge, OAT-v2 is a proven system that enables scalable and robust experimentation in experimental and real-world deployment. OAT-v2 provides open models and software for research and commercial applications to enable the future of multimodal virtual assistants across diverse applications and types of rich interaction.

cs.IR↗

arXiv2023

A quantum coherent spin in a two-dimensional material at room temperature

Quantum networks and sensing require solid-state spin-photon interfaces that combine single-photon generation and long-lived spin coherence with scalable device integration, ideally at ambient conditions. Despite rapid progress reported across several candidate systems, those possessing quantum coherent single spins at room temperature remain extremely rare. Here, we report quantum coherent control under ambient conditions of a single-photon emitting defect spin in a a two-dimensional material, hexagonal boron nitride. We identify that the carbon-related defect has a spin-triplet electronic ground-state manifold. We demonstrate that the spin coherence is governed predominantly by coupling to only a few proximal nuclei and is prolonged by decoupling protocols. Our results allow for a room-temperature spin qubit coupled to a multi-qubit quantum register or quantum sensor with nanoscale sample proximity.

cond-mat.mes-hall↗

arXiv2023

mmSense: Detecting Concealed Weapons with a Miniature Radar Sensor

For widespread adoption, public security and surveillance systems must be accurate, portable, compact, and real-time, without impeding the privacy of the individuals being observed. Current systems broadly fall into two categories -- image-based which are accurate, but lack privacy, and RF signal-based, which preserve privacy but lack portability, compactness and accuracy. Our paper proposes mmSense, an end-to-end portable miniaturised real-time system that can accurately detect the presence of concealed metallic objects on persons in a discrete, privacy-preserving modality. mmSense features millimeter wave radar technology, provided by Google's Soli sensor for its data acquisition, and TransDope, our real-time neural network, capable of processing a single radar data frame in 19 ms. mmSense achieves high recognition rates on a diverse set of challenging scenes while running on standard laptop hardware, demonstrating a significant advancement towards creating portable, cost-effective real-time radar based surveillance systems.

cs.LG↗

arXiv2021

Forward and Inverse models in HCI:Physical simulation and deep learning for inferring 3D finger pose

We outline the role of forward and inverse modelling approaches in the design of human--computer interaction systems. Causal, forward models tend to be easier to specify and simulate, but HCI requires solutions of the inverse problem. We infer finger 3D position $(x,y,z)$ and pose (pitch and yaw) on a mobile device using capacitive sensors which can sense the finger up to 5cm above the screen. We use machine learning to develop data-driven models to infer position, pose and sensor readings, based on training data from: 1. data generated by robots, 2. data from electrostatic simulators 3. human-generated data. Machine learned emulation is used to accelerate the electrostatic simulation performance by a factor of millions. We combine a Conditional Variational Autoencoder with domain expertise/models experimentally collected data. We compare forward and inverse model approaches to direct inference of finger pose. The combination gives the most accurate reported results on inferring 3D position and pose with a capacitive sensor on a mobile device.

cs.HC↗

arXiv2012

Near dispersion-less surface plasmon polariton resonances at a metal-dielectric interface

Omni-directional light coupling to surface plasmon polariton (SPP) modes to make use of plasmon mediated near-field enhancement is challenging. We report possibility of near dispersion-less modes in structures with unpatterned metal-dielectric interfaces having 2-D dielectric patterns on top. We show that the position and dispersion of the excited modes can be controlled by the excitation geometry and the 2-D pattern. The anti-crossings resulting from the in-plane coupling of different SPP modes are also shown.

cond-mat.mtrl-sci↗