arXiv subjects

Huyen Do

Publications and source records attributed to Huyen Do.

4 recordsLinked to original sources

arXiv2026

Circuit-Level Noise Estimation via Shuttling in Plaquette Circuits

We present a method for estimating QEC circuit-level noise levels assuming that only single-shot measurements are available (e.g. measurements are slow and performed in a zoned/parallel fashion), and that lower level quantum hardware calibration is not possible (e.g. cloud access) or not feasible (e.g. large scale computing). We develop and run surface code plaquette experiments using two syndrome qubit configurations: FRESH, involving fresh qubits for each plaquette repetition, and RECYCLE, reusing qubits. To validate our approach, we compile plaquettes to ion-trap (IonQ Aria1) native gate set and apply hardware-aware rewrite templates to reduce circuit depth and execution time. We also run the experiments on a non-shuttling, superconducting processor (IBM Torino). We estimate circuit-level noise rates from the resulting single-shot plaquette measurement statistics, and conclude numerically about the viability of low-depth QEC experiments.

quant-ph↗

arXiv2024

f-GAN: A frequency-domain-constrained generative adversarial network for PPG to ECG synthesis

Electrocardiograms (ECGs) and photoplethysmograms (PPGs) are generally used to monitor an individual's cardiovascular health. In clinical settings, ECGs and fingertip PPGs are the main signals used for assessing cardiovascular health, but the equipment necessary for their collection precludes their use in daily monitoring. Although PPGs obtained from wrist-worn devices are susceptible to noise due to motion, they have been widely used to continuously monitor cardiovascular health because of their convenience. Therefore, we would like to combine the ease with which PPGs can be collected with the information that ECGs provide about cardiovascular health by developing models to synthesize ECG signals from paired PPG signals. We tackled this problem using generative adversarial networks (GANs) and found that models trained using the original GAN formulations can be successfully used to synthesize ECG signals from which heart rate can be extracted using standard signal processing pipelines. Incorporating a frequency-domain constraint to model training improved the stability of model performance and also the performance on heart rate estimation.

eess.SP↗

arXiv2013

A Metric-learning based framework for Support Vector Machines and Multiple Kernel Learning

Most metric learning algorithms, as well as Fisher's Discriminant Analysis (FDA), optimize some cost function of different measures of within-and between-class distances. On the other hand, Support Vector Machines(SVMs) and several Multiple Kernel Learning (MKL) algorithms are based on the SVM large margin theory. Recently, SVMs have been analyzed from SVM and metric learning, and to develop new algorithms that build on the strengths of each. Inspired by the metric learning interpretation of SVM, we develop here a new metric-learning based SVM framework in which we incorporate metric learning concepts within SVM. We extend the optimization problem of SVM to include some measure of the within-class distance and along the way we develop a new within-class distance measure which is appropriate for SVM. In addition, we adopt the same approach for MKL and show that it can be also formulated as a Mahalanobis metric learning problem. Our end result is a number of SVM/MKL algorithms that incorporate metric learning concepts. We experiment with them on a set of benchmark datasets and observe important predictive performance improvements.

cs.LG↗

arXiv2012

A metric learning perspective of SVM: on the relation of SVM and LMNN

Support Vector Machines, SVMs, and the Large Margin Nearest Neighbor algorithm, LMNN, are two very popular learning algorithms with quite different learning biases. In this paper we bring them into a unified view and show that they have a much stronger relation than what is commonly thought. We analyze SVMs from a metric learning perspective and cast them as a metric learning problem, a view which helps us uncover the relations of the two algorithms. We show that LMNN can be seen as learning a set of local SVM-like models in a quadratic space. Along the way and inspired by the metric-based interpretation of SVM s we derive a novel variant of SVMs, epsilon-SVM, to which LMNN is even more similar. We give a unified view of LMNN and the different SVM variants. Finally we provide some preliminary experiments on a number of benchmark datasets in which show that epsilon-SVM compares favorably both with respect to LMNN and SVM.

cs.LG↗