arXiv subjects

Quyen Tran

Publications and source records attributed to Quyen Tran.

22 records · Page 2Linked to original sources

arXiv2024

Distributed Quantum Computing in Silicon

Commercially impactful quantum algorithms such as quantum chemistry and Shor's algorithm require a number of qubits and gates far beyond the capacity of any existing quantum processor. Distributed architectures, which scale horizontally by networking modules, provide a route to commercial utility and will eventually surpass the capability of any single quantum computing module. Such processors consume remote entanglement distributed between modules to realize distributed quantum logic. Networked quantum computers will therefore require the capability to rapidly distribute high fidelity entanglement between modules. Here we present preliminary demonstrations of some key distributed quantum computing protocols on silicon T centres in isotopically-enriched silicon. We demonstrate the distribution of entanglement between modules and consume it to apply a teleported gate sequence, establishing a proof-of-concept for T centres as a distributed quantum computing and networking platform.

quant-ph↗

arXiv2024

Class-Prototype Conditional Diffusion Model with Gradient Projection for Continual Learning

Mitigating catastrophic forgetting is a key hurdle in continual learning. Deep Generative Replay (GR) provides techniques focused on generating samples from prior tasks to enhance the model's memory capabilities using generative AI models ranging from Generative Adversarial Networks (GANs) to the more recent Diffusion Models (DMs). A major issue is the deterioration in the quality of generated data compared to the original, as the generator continuously self-learns from its outputs. This degradation can lead to the potential risk of catastrophic forgetting (CF) occurring in the classifier. To address this, we propose the Gradient Projection Class-Prototype Conditional Diffusion Model (GPPDM), a GR-based approach for continual learning that enhances image quality in generators and thus reduces the CF in classifiers. The cornerstone of GPPDM is a learnable class prototype that captures the core characteristics of images in a given class. This prototype, integrated into the diffusion model's denoising process, ensures the generation of high-quality images of the old tasks, hence reducing the risk of CF in classifiers. Moreover, to further mitigate the CF of diffusion models, we propose a gradient projection technique tailored for the cross-attention layer of diffusion models to maximally maintain and preserve the representations of old task data in the current task as close as possible to their representations when they first arrived. Our empirical studies on diverse datasets demonstrate that our proposed method significantly outperforms existing state-of-the-art models, highlighting its satisfactory ability to preserve image quality and enhance the model's memory retention.

cs.LG↗

arXiv2022

Unique Ferroelectric Fatigue Behavior and Exceptional High Temperature Retention in Al0.93B0.07N Films

This paper reports the fatigue and retention behavior for Al1-xBxN thin films, a member of the novel family of wurtzite ferroelectrics, with an emphasis on the role of capacitor architecture. By modifying the capacitor architecture, and thus thermal and electrical boundary conditions, we create insight regarding the relative importance of intrinsic and extrinsic contributors to the degradation tendencies. Our experiments suggest that bipolar cycling of metal (Pt/W)/Al0.93B0.07N/W/Al2O3 film stacks first induced wake-up, then a region of constant switchable polarization. On additional cycling, the film leakage current increased, and then films underwent dielectric breakdown. For unpatterned first generation Al0.93B0.07N films with 100 nm thick Pt top electrodes survive ~104 bipolar cycles, whereas films with 1000 nm W top electrodes survive ~10^5 cycles before thermal dielectric breakdown. Sentaurus modeling was used to design an SU8 field plate which improved the performance to ~10^6 fatigue cycles. It was found that the thermal failures during fatigue were not due to surface flashover events but were associated with hard breakdown events in the dielectric. The films showed excellent retention of the stored polarization state. As expected, data retention was slightly inferior in the opposite state (OS) measurements. However, it is noted that even after 3.6x10^6 sec (1000 hr). at 200°C, the OS signal margin still exceeded 200 uC/cm2. The predicted OS retention is 82% after 10 years baking at 200oC.

physics.app-ph↗

arXiv2021

From Implicit to Explicit feedback: A deep neural network for modeling sequential behaviours and long-short term preferences of online users

In this work, we examine the advantages of using multiple types of behaviour in recommendation systems. Intuitively, each user has to do some implicit actions (e.g., click) before making an explicit decision (e.g., purchase). Previous studies showed that implicit and explicit feedback have different roles for a useful recommendation. However, these studies either exploit implicit and explicit behaviour separately or ignore the semantic of sequential interactions between users and items. In addition, we go from the hypothesis that a user's preference at a time is a combination of long-term and short-term interests. In this paper, we propose some Deep Learning architectures. The first one is Implicit to Explicit (ITE), to exploit users' interests through the sequence of their actions. And two versions of ITE with Bidirectional Encoder Representations from Transformers based (BERT-based) architecture called BERT-ITE and BERT-ITE-Si, which combine users' long- and short-term preferences without and with side information to enhance user representation. The experimental results show that our models outperform previous state-of-the-art ones and also demonstrate our views on the effectiveness of exploiting the implicit to explicit order as well as combining long- and short-term preferences in two large-scale datasets.

cs.LG↗