arXiv subjects

Christopher T. -K. Lew

Publications and source records attributed to Christopher T. -K. Lew.

5 recordsLinked to original sources

arXiv2026

Magnetic Communication with an Acoustically Actuated Magnetoelectric Resonator and a Quantum Diamond Magnetometer

Wireless communication via propagating magnetic fields is a communication modality that has recently garnered significant interest for short-to-medium range communication in conductive mediums, such as underwater and underground, where existing approaches utilizing electric fields are highly inefficient. Typical implementations of magnetic communication make use of loop antennas as both the transmitter and receiver, with the sensitivity and frequency response scaling with and inversely with the loop cross-sectional area, respectively. Here, we explore an alternative hybrid magnetic communication system consisting of an highly radiation efficient and compact acoustically actuated magnetoelectric resonator as the transmitter, and a highly sensitive micrometer scale quantum magnetometer based on nitrogen-vacancy centers in diamond as the receiver, with their core properties unconstrained by size. We demonstrate amplitude and phase-encoded transmission and reception of AC magnetic fields at $f_{\mathrm{AC}}$ = 20 kHz, achieving a sensitivity of 50 pT/$\sqrt{\mathrm{Hz}}$ and 1.2 mrad/$\sqrt{\mathrm{Hz}}$, respectively. This work establishes the use of hybrid magnetoelectric resonator and quantum diamond magnetometer communication system as a viable alternative to existing loop-based approaches.

cond-mat.mes-hall↗

arXiv2026

Power sensitivity of broadband radiofrequency detectors based on quantum diamond spins

Nitrogen-vacancy (NV) centres in diamond can be used to detect radiofrequency (RF) signals through coupling of the RF magnetic field with the NV spins, combined with optical readout of the spin state. The sensitivity of such RF detectors has so far been mainly studied in terms of magnetic field sensitivity, which is relevant when the RF signal is generated by a near-field source. However, for applications where the RF input is delivered externally, a more relevant quantity is the sensitivity in terms of the input RF power. Here we theoretically analyse the power sensitivity of NV-based RF detectors as a function of the RF-spin interface geometry. We derive scaling laws of the power sensitivity for both slope-detection and variance-detection RF sensing protocols, and for various noise regimes. We find that, in most scenarios, the power sensitivity scales inversely with the characteristic physical dimension of the RF-spin interface, for instance the width of a coplanar waveguide or the diameter of a loop antenna. In other words, the smaller the structure and the probed NV volume, the better the power sensitivity, which is contrary to the case of magnetic field sensitivity. Lastly, we numerically estimate that photon shot noise limited sensitivities of 10^{-20} W Hz^{-1} (slope) and 10^{-12} W Hz^{-1/2} (variance) are achievable. This work lays the groundwork for further optimisation of NV-based RF detectors.

quant-ph↗

arXiv2026

Electrically detected magnetic resonance of $^{75}$As magnetic clock transitions in silicon

Magnetic clock transitions (CTs), defined by vanishing first-order sensitivity of the transition frequency to magnetic field fluctuations, provide a powerful route to suppress decoherence in donor spin systems. Here, we present the observation of magnetic field CTs from an ensemble of near-surface $^{75}$As ($I = 3/2$) spins in silicon using low-field ($< 10$~mT) continuous-wave electrically detected magnetic resonance (EDMR). As the CT condition is approached, pronounced linewidth broadening is observed, consistent with a donor Hamiltonian informed linewidth model. These results establish low-field EDMR as a sensitive probe of CTs in near-surface donor systems relevant to silicon-based quantum devices.

quant-ph↗

arXiv2026

Real-time Amplitude and Phase Estimation of AC Fields with Diamond Spins

Nitrogen-vacancy centers in diamond have been shown to be capable of detecting AC magnetic fields with high sensitivity, spectral resolution, and spatial resolution. However, most studies so far have focused on the regime of time-averaged or time-correlated measurements, while little attention has been paid to the single-shot regime. Here we show that the amplitude and phase of an AC field can be retrieved from a single pair of two consecutive measurements. We demonstrate this concept by measuring a 4 MHz AC field with a per-shot amplitude and phase sensitivity of 78 nT and 63 mrad, respectively, at a temporal resolution of 320 us. We also investigate the effects and quantify the errors resulting from probe frequency detunings, as well as operating in the strong field regime. Moreover, we showcase the ability of the measurement protocol to dynamically change the probe frequency in real-time. This work advances the use of NV centers for real-time measurements of AC magnetic fields.

cond-mat.mes-hall↗

arXiv2025

Machine learning assisted tracking of magnetic objects using quantum diamond magnetometry

Remote magnetic sensing can be used to monitor the position of objects in real-time, enabling ground transport monitoring, underground infrastructure mapping and hazardous detection. However, magnetic signals are typically weak and complex, requiring sophisticated physical models to analyze them and a detailed knowledge of the system under study, factors that are frequently unavailable. In this work, we provide a solution to these limitations by demonstrating a Machine Learning (ML) method that can be trained exclusively on experimental data, without the need of any physical model, to predict the position of a magnetic target in real-time. The target can be any object with a magnetic signal above the floor noise, and in this case we use a quantum diamond magnetometer to track variations of few hundreds of nanoteslas produced by an elevator moving along a single axis. The one-dimensional movement is a simple yet challenging scenario, resembling realistic environments such as high buildings, tunnels or train circuits, and is the first step towards building broader applications. Our ML algorithm can be trained in approximately 40 min, achieving over 80% accuracy in predicting the target's position at a rate of 10 Hz, for a positional error tolerance of 30 cm, which is a precise distance compared to the 4-meter spacing between parking levels. Our results open up the possibility to apply this ML method more generally for real-time monitoring of magnetic objects, which will broaden the scope of magnetic detection applications.

physics.app-ph↗