arXiv subjects

Quan Hoang

Publications and source records attributed to Quan Hoang.

3 recordsLinked to original sources

arXiv2025

Medusa: Detecting and Removing Failures for Scalable Quantum Computing

Quantum circuits will experience failures that lead to computational errors. We introduce Medusa, an automated compilation method for lowering a circuit's failure rate. Medusa uses flags to predict the absence of high-weight errors. Our method can numerically upper bound the failure rate of a circuit in the presence of flags, and fine tune the fault-tolerance of the flags in order to reach this bound. We assume the flags can have an increased fault-tolerance as a result of applying surface QECs to the gates interacting with them. We use circuit level depolarizing noise to evaluate the effectiveness of these flags in revealing the absence of the high-weight stabilizers. Medusa reduces the cost of quantum-error-correction (QEC) because the underlying circuit has a lower failure rate. We benchmark our approach using structured quantum circuits representative of ripple-carry adders. In particular, our flag scheme demonstrates that for adder-like circuits, the failure rate of large-scale implementations can be lowered to fit the failure rates of smaller-scale circuits. We show numerically that a slight improvement in the local fault-tolerance of the flag-qubits can lead to a reduction in the overall failure rate of the entire quantum circuit.

quant-ph↗

arXiv2018

Predicting Movie Genres Based on Plot Summaries

This project explores several Machine Learning methods to predict movie genres based on plot summaries. Naive Bayes, Word2Vec+XGBoost and Recurrent Neural Networks are used for text classification, while K-binary transformation, rank method and probabilistic classification with learned probability threshold are employed for the multi-label problem involved in the genre tagging task.Experiments with more than 250,000 movies show that employing the Gated Recurrent Units (GRU) neural networks for the probabilistic classification with learned probability threshold approach achieves the best result on the test set. The model attains a Jaccard Index of 50.0%, a F-score of 0.56, and a hit rate of 80.5%.

cs.CL↗

arXiv2017

Multi-Generator Generative Adversarial Nets

We propose a new approach to train the Generative Adversarial Nets (GANs) with a mixture of generators to overcome the mode collapsing problem. The main intuition is to employ multiple generators, instead of using a single one as in the original GAN. The idea is simple, yet proven to be extremely effective at covering diverse data modes, easily overcoming the mode collapse and delivering state-of-the-art results. A minimax formulation is able to establish among a classifier, a discriminator, and a set of generators in a similar spirit with GAN. Generators create samples that are intended to come from the same distribution as the training data, whilst the discriminator determines whether samples are true data or generated by generators, and the classifier specifies which generator a sample comes from. The distinguishing feature is that internal samples are created from multiple generators, and then one of them will be randomly selected as final output similar to the mechanism of a probabilistic mixture model. We term our method Mixture GAN (MGAN). We develop theoretical analysis to prove that, at the equilibrium, the Jensen-Shannon divergence (JSD) between the mixture of generators' distributions and the empirical data distribution is minimal, whilst the JSD among generators' distributions is maximal, hence effectively avoiding the mode collapse. By utilizing parameter sharing, our proposed model adds minimal computational cost to the standard GAN, and thus can also efficiently scale to large-scale datasets. We conduct extensive experiments on synthetic 2D data and natural image databases (CIFAR-10, STL-10 and ImageNet) to demonstrate the superior performance of our MGAN in achieving state-of-the-art Inception scores over latest baselines, generating diverse and appealing recognizable objects at different resolutions, and specializing in capturing different types of objects by generators.

cs.LG↗