Predicting quantum emitter fluctuations with time-series forecasting models.

2D materials have important fundamental properties allowing for their use in many potential applications, including quantum computing. Various Van der Waals materials, including Tungsten disulfide (WS2), have been employed to showcase attractive device applications such as light emitting diodes, lasers and optical modulators. To maximize the utility and value of integrated quantum photonics, the wavelength, polarization and intensity of the photons from a quantum emission (QE) must be stable. However, random variation of emission energy, caused by the inhomogeneity in the local environment, is a major challenge for all solid-state single photon emitters. In this work, we assess the random nature of the quantum fluctuations, and we present time series forecasting deep learning models to analyse and predict QE fluctuations for the first time. Our trained models can roughly follow the actual trend of the data and, under certain data processing conditions, can predict peaks and dips of the fluctuations. The ability to anticipate these fluctuations will allow physicists to harness quantum fluctuation characteristics to develop novel scientific advances in quantum computing that will greatly benefit quantum technologies.

Automatic detection of multilayer hexagonal boron nitride in optical images using deep learning-based computer vision.

Computer vision algorithms can quickly analyze numerous images and identify useful information with high accuracy. Recently, computer vision has been used to identify 2D materials in microscope images. 2D materials have important fundamental properties allowing for their use in many potential applications, including many in quantum information science and engineering. One such material is hexagonal boron nitride (hBN), an isomorph of graphene with a very indistinguishable layered structure. In order to use these materials for research and product development, the most effective method is mechanical exfoliation where single-layer 2D crystallites must be prepared through an exfoliation procedure and then identified using reflected light optical microscopy. Performing these searches manually is a time-consuming and tedious task. Deploying deep learning-based computer vision algorithms for 2D material search can automate the flake detection task with minimal need for human intervention. In this work, we have implemented a new deep learning pipeline to classify crystallites of hBN based on coarse thickness classifications in reflected-light optical micrographs. We have used DetectoRS as the object detector and trained it on 177 images containing hexagonal boron nitride (hBN) flakes of varying thickness. The trained model achieved a high detection accuracy for the rare category of thin flakes ([Formula: see text] atomic layers thick). Further analysis shows that our proposed pipeline could be generalized to various microscope settings and is robust against changes in color or substrate background.

AF detection from ECG recordings using feature selection, sparse coding, and ensemble learning.

OBJECTIVE: The objective of this paper is to provide an algorithm for accurate, automated detection of atrial fibrillation (AF) from ECG signals. Four types of ECG signals are considered: normal signals, signals representing symptoms of AF, other signals, and noisy signals. This paper represents follow-up work to the authors' entry in the 2017 PhysioNet Challenge as reported in the 2017 Computing in Cardiology Conference. APPROACH: Our approach involves extracting features from the ECG waveform and training a machine learning classifier. In feature extraction, we calculate several statistical features related to the ECG signal and fiduciary points. We also used a disciplined method of feature selection to reduce the dimensionality of the feature space. We also employ sparse coding as an unsupervised feature extraction tool. The classifier we use is a decision tree-based ensemble learning classifier. MAIN RESULTS: When applied to the hidden test data reserved by the PhysioNet Challenge organizers, our classifier reports F1 scores of 0.91, 0.78, and 0.71 for the Normal, AF, and Other classes, respectively. The overall test score is 0.80, and is obtained by averaging the F1 scores for these three classes. SIGNIFICANCE: This work demonstrates that feature selection and ensemble learning can be used to improve the performance of ECG-based classification of AF.

Combining sparse coding and time-domain features for heart sound classification.

OBJECTIVE: This paper builds upon work submitted as part of the 2016 PhysioNet/CinC Challenge, which used sparse coding as a feature extraction tool on audio PCG data for heart sound classification. APPROACH: In sparse coding, preprocessed data is decomposed into a dictionary matrix and a sparse coefficient matrix. The dictionary matrix represents statistically important features of the audio segments. The sparse coefficient matrix is a mapping that represents which features are used by each segment. Working in the sparse domain, we train support vector machines (SVMs) for each audio segment (S1, systole, S2, diastole) and the full cardiac cycle. We train a sixth SVM to combine the results from the preliminary SVMs into a single binary label for the entire PCG recording. In addition to classifying heart sounds using sparse coding, this paper presents two novel modifications. The first uses a matrix norm in the dictionary update step of sparse coding to encourage the dictionary to learn discriminating features from the abnormal heart recordings. The second combines the sparse coding features with time-domain features in the final SVM stage. MAIN RESULTS: The original algorithm submitted to the challenge achieved a cross-validated mean accuracy (MAcc) score of 0.8652 (Se = 0.8669 and Sp = 0.8634). After incorporating the modifications new to this paper, we report an improved cross-validated MAcc of 0.8926 (Se = 0.9007 and Sp = 0.8845). SIGNIFICANCE: Our results show that sparse coding is an effective way to define spectral features of the cardiac cycle and its sub-cycles for the purpose of classification. In addition, we demonstrate that sparse coding can be combined with additional feature extraction methods to improve classification accuracy.

Frequency dependence of slab coupled optical sensor sensitivity.

This paper presents the frequency-dependent sensitivity of slab-coupled optical fiber sensors (SCOSs). This dependence is caused by the frequency characteristics of the relative permittivity. We show experimentally the frequency dependence of SCOS sensitivity for frequencies in the range of 1 kHz to 1 MHz for SCOS fabricated with both potassium titanyl phosphate (KTP) and lithium niobate (LiNbO(3)). We conclude that x-cut KTP SCOSs are preferred for measuring fields above 300 kHz as they are 1.55× more sensitive than x-cut LiNbO(3) SCOSs to the higher frequency fields. However, since KTP SCOSs experience increasing permittivity for low frequencies, SCOSs made with LiNbO(3) may be used for low frequency sensing applications due to their flat sensitivity response. For a 10 kHz electric field, an x cut LiNbO(3) SCOS is approximately 3.43× more sensitive than an x-cut KTP SCOS.