Scaling of entanglement at quantum phase transition for two-dimensional array of quantum dots

With Hubbard model, the entanglement scaling behavior in a two-dimensional itinerant system is investigated. It has been found that, on the two sides of the critical point denoting an inherent quantum phase transition (QPT), the entanglement follows different scalings with the size just as an order parameter does. This fact reveals the subtle role played by the entanglement in QPT as a fungible physical resource

Federated Learning of Large Language Models with Parameter-Efficient Prompt Tuning and Adaptive Optimization

Federated learning (FL) is a promising paradigm to enable collaborative model training with decentralized data. However, the training process of Large Language Models (LLMs) generally incurs the update of significant parameters, which limits the applicability of FL techniques to tackle the LLMs in real scenarios. Prompt tuning can significantly reduce the number of parameters to update, but it either incurs performance degradation or low training efficiency. The straightforward utilization of prompt tuning in the FL often raises non-trivial communication costs and dramatically degrades performance. In addition, the decentralized data is generally non-Independent and Identically Distributed (non-IID), which brings client drift problems and thus poor performance. This paper proposes a Parameter-efficient prompt Tuning approach with Adaptive Optimization, i.e., FedPepTAO, to enable efficient and effective FL of LLMs. First, an efficient partial prompt tuning approach is proposed to improve performance and efficiency simultaneously. Second, a novel adaptive optimization method is developed to address the client drift problems on both the device and server sides to enhance performance further. Extensive experiments based on 10 datasets demonstrate the superb performance (up to 60.8\% in terms of accuracy) and efficiency (up to 97.59\% in terms of training time) of FedPepTAO compared with 9 baseline approaches. Our code is available at https://github.com/llm-eff/FedPepTAO.Comment: 18 pages, accepted by EMNLP 202

Strain-tunable entangled-light-emitting diodes with high yield and fast operation speed

Triggered sources of entangled photons play crucial roles in almost any existing protocol of quantum information science. The possibility to generate these non-classical states of light with high speed and using electrical pulses could revolutionize the field. Entangled-light-emitting-diodes (ELEDs) based on semiconductor quantum dots (QDs) are at present the only devices that can address this task 5. However, ELEDs are plagued by a source of randomness that hampers their practical exploitation in the foreseen applications: the very low probability (~10-2) of finding QDs with sufficiently small fine-structure-splitting for entangled-photon-generation. Here, we overcome this hurdle by introducing the first strain-tunable ELEDs (S-ELEDs) that exploit piezoelectric-induced strains to tune QDs for entangled-photon-generation. We demonstrate that up to 30% of the QDs in S-ELEDs emit polarization-entangled photon pairs with entanglement-fidelities as high as f+ = 0.83(5). Driven at the highest operation speed of 400 MHz ever reported so far, S-ELEDs emerge as unique devices for high-data rate entangled-photon applications.Comment: 28 pages in total, including supplementary information. 5 figure

The role of the amygdala during emotional processing in Huntington's disease: from pre-manifest to late stage disease.

BACKGROUND: Deficits in emotional processing can be detected in the pre-manifest stage of Huntington's disease and negative emotion recognition has been identified as a predictor of clinical diagnosis. The underlying neuropathological correlates of such deficits are typically established using correlative structural MRI studies. This approach does not take into consideration the impact of disruption to the complex interactions between multiple brain circuits on emotional processing. Therefore, exploration of the neural substrates of emotional processing in pre-manifest HD using fMRI connectivity analysis may be a useful way of evaluating the way brain regions interrelate in the period prior to diagnosis. METHODS: We investigated the impact of predicted time to disease onset on brain activation when participants were exposed to pictures of faces with angry and neutral expressions, in 20 pre-manifest HD gene carriers and 23 healthy controls. On the basis of the results of this initial study went on to look at amygdala dependent cognitive performance in 79 Huntington's disease patients from a cross-section of disease stages (pre-manifest to late disease) and 26 healthy controls, using a validated theory of mind task: "the Reading the Mind in the Eyes Test" which has been previously been shown to be amygdala dependent. RESULTS: Psychophysiological interaction analysis identified reduced connectivity between the left amygdala and right fusiform facial area in pre-manifest HD gene carriers compared to controls when viewing angry compared to neutral faces. Change in PPI connectivity scores correlated with predicted time to disease onset (r=0.45, p<0.05). Furthermore, performance on the "Reading the Mind in the Eyes Test" correlated negatively with proximity to disease onset and became progressively worse with each stage of disease. CONCLUSION: Abnormalities in the neural networks underlying social cognition and emotional processing can be detected prior to clinical diagnosis in Huntington's disease. Connectivity between the amygdala and other brain regions is impacted by the disease process in pre-manifest HD and may therefore be a useful way of identifying participants who are approaching a clinical diagnosis. Furthermore, the "Reading the Mind in the Eyes Test" is a surrogate measure of amygdala function that is clinically useful across the entire cross-section of disease stages in HD.The work included in this manuscript has been partially funded by financial support from the NIHR Cambridge Biomedical Research Centre and the Cambridge University NHS Foundation Trust. JBR is supported by the Wellcome Trust (088324).This is the final version of the article. It first appeared at http://dx.doi.org/10.1016/j.neuropsychologia.2015.02.01

Electrically-Pumped Wavelength-Tunable GaAs Quantum Dots Interfaced with Rubidium Atoms

We demonstrate the first wavelength-tunable electrically-pumped source of non-classical light that can emit photons with wavelength in resonance with the D2 transitions of 87Rb atoms. The device is fabricated by integrating a novel GaAs single-quantum-dot light-emitting-diode (LED) onto a piezoelectric actuator. By feeding the emitted photons into a 75-mm-long cell containing warm 87Rb atom vapor, we observe slow-light with a temporal delay of up to 3.4 ns. In view of the possibility of using 87Rb atomic vapors as quantum memories, this work makes an important step towards the realization of hybrid-quantum systems for future quantum networks

Set It and Forget It! Turnkey ECC for Instant Integration

Historically, Elliptic Curve Cryptography (ECC) is an active field of applied cryptography where recent focus is on high speed, constant time, and formally verified implementations. While there are a handful of outliers where all these concepts join and land in real-world deployments, these are generally on a case-by-case basis: e.g.\ a library may feature such X25519 or P-256 code, but not for all curves. In this work, we propose and implement a methodology that fully automates the implementation, testing, and integration of ECC stacks with the above properties. We demonstrate the flexibility and applicability of our methodology by seamlessly integrating into three real-world projects: OpenSSL, Mozilla's NSS, and the GOST OpenSSL Engine, achieving roughly 9.5x, 4.5x, 13.3x, and 3.7x speedup on any given curve for key generation, key agreement, signing, and verifying, respectively. Furthermore, we showcase the efficacy of our testing methodology by uncovering flaws and vulnerabilities in OpenSSL, and a specification-level vulnerability in a Russian standard. Our work bridges the gap between significant applied cryptography research results and deployed software, fully automating the process