Quantum computing with quantum-Hall edge state interferometry

Electron interferometers based on Hall edge states (ESs) proved to be robust demonstrators of the coherent quantum dynamics of carriers. Several proposals to expose their capability to build and control quantum entanglement and to exploit them as building block for quantum computing devices has been presented. Here, we review the time-dependent numerical modeling of Hall interferometers operating at the single-carrier level at integer filling factor (FF). By defining the qubit state either as the spatial localization (at FF 1) or the Landau index (at FF 2) of a single carrier propagating in the ES, we show how a generic one-qubit rotation can be realized. By a proper design of the two-dimensional electron gas potential landscape, an entangling two-qubit gate can be implemented by exploiting Coulomb interaction, thus realizing a universal set of quantum gates. We also assess how the shape of the edge confining potential affects the visibility of the quantum transformations

Two-electron selective coupling in an edge-state based conditional phase shifter

We investigate the effect of long-range Coulomb interaction on the two-electron scattering in the integer quantum Hall regime at bulk filling factor two.We compute the dynamics of the exact two-particle wave function by means of a parallel version of the split-step Fourier method in a 2D potential background reproducing the effect of depleting gates in a realistic heterostructure, with the charge carrier represented by a localized wave packet of edge states.We compare the spatial shift induced by Coulomb repulsion in the final two-electron wave function for two indistinguishable electrons initialized in different configurations according to their Landau index and analyze their bunching probability and the effect of screening. We finally prove the feasibility of this device as a two-qubit conditional phase shifter able to generate controlled entanglement from product states

Coulomb and exchange interaction effects on the exact two-electron dynamics in the Hong-Ou-Mandel interferometer based on Hall edge states

The electronic Hong-Ou-Mandel interferometer in the integer quantum Hall regime is an ideal system to probe the building up of quantum correlations between charge carriers and it has been proposed as a viable platform for quantum computing gates. Using a parallel implementation of the split-step Fourier method, we simulated the antibunching of two interacting fermionic wave packets impinging on a quantum point contact. Numerical results of the exact approach are compared with a simplified theoretical model based on one-dimensional scattering formalism. We show that, for strongly localized wave packets in a full-scale geometry, the Coulomb repulsion dominates over the exchange energy, this effect being strongly dependent on the energy broadening of the particles. We define analytically the spatial entanglement between the two regions of the quantum point contact, and obtain quantitatively its entanglement-generation capabilities

A Proposal for Evading the Measurement Uncertainty in Classical and Quantum Computing: Application to a Resonant Tunneling Diode and a Mach-Zehnder Interferometer

Measuring properties of quantum systems is governed by a stochastic (collapse or state-reduction) law that unavoidably yields an uncertainty (variance) associated with the corresponding mean values. This non-classical source of uncertainty is known to be manifested as noise in the electrical current of nanoscale electron devices, and hence it can flaw the good performance of more complex quantum gates. We propose a protocol to alleviate this quantum uncertainty that consists of (i) redesigning the device to accommodate a large number of electrons inside the active region, either by enlarging the lateral or longitudinal areas of the device and (ii) re-normalizing the total current to the number of electrons. How the above two steps can be accommodated using the present semiconductor technology has been discussed and numerically studied for a resonant tunneling diode and a Mach-Zehnder interferometer, for classical and quantum computations, respectively. It is shown that the resulting protocol formally resembles the so-called collective measurements, although, its practical implementation is substantially different

JNK1 Phosphorylates SIRT1 and Promotes Its Enzymatic Activity

SIRT1 is a NAD-dependent deacetylase that regulates a variety of pathways including the stress protection pathway. SIRT1 deacetylates a number of protein substrates, including histones, FOXOs, PGC-1α, and p53, leading to cellular protection. We identified a functional interaction between cJUN N-terminal kinase (JNK1) and SIRT1 by coimmunoprecipitation of endogenous proteins. The interaction between JNK1 and SIRT1 was identified under conditions of oxidative stress and required activation of JNK1 via phosphorylation. Modulation of SIRT1 activity or protein levels using nicotinamide or RNAi did not modify JNK1 activity as measured by its ability to phosphorylate cJUN. In contrast, human SIRT1 was phosphorylated by JNK1 on three sites: Ser27, Ser47, and Thr530 and this phosphorylation of SIRT1 increased its nuclear localization and enzymatic activity. Surprisingly, JNK1 phosphorylation of SIRT1 showed substrate specificity resulting in deacetylation of histone H3, but not p53. These findings identify a mechanism for regulation of SIRT1 enzymatic activity in response to oxidative stress and shed new light on its role in the stress protection pathway

A tool to overcome technical barriers for bias assessment in human language technologies

Automatic processing of language is becoming pervasive in our lives, oftentaking central roles in our decision making, like choosing the wording for ourmessages and mails, translating our readings, or even having full conversationswith us. Word embeddings are a key component of modern natural languageprocessing systems. They provide a representation of words that has boosted theperformance of many applications, working as a semblance of meaning. Wordembeddings seem to capture a semblance of the meaning of words from raw text,but, at the same time, they also distill stereotypes and societal biases whichare subsequently relayed to the final applications. Such biases can bediscriminatory. It is very important to detect and mitigate those biases, toprevent discriminatory behaviors of automated processes, which can be much moreharmful than in the case of humans because their of their scale. There arecurrently many tools and techniques to detect and mitigate biases in wordembeddings, but they present many barriers for the engagement of people withouttechnical skills. As it happens, most of the experts in bias, either socialscientists or people with deep knowledge of the context where bias is harmful,do not have such skills, and they cannot engage in the processes of biasdetection because of the technical barriers. We have studied the barriers inexisting tools and have explored their possibilities and limitations withdifferent kinds of users. With this exploration, we propose to develop a toolthat is specially aimed to lower the technical barriers and provide theexploration power to address the requirements of experts, scientists and peoplein general who are willing to audit these technologies.Fil: Alemany, Laura Alonso. Fundación Via Libre; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; ArgentinaFil: Benotti, Luciana. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Fundación Via Libre; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Gonzalez, Lucía. Fundación Via Libre; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; ArgentinaFil: Maina, Hernán Javier. Fundación Via Libre; Argentina. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; ArgentinaFil: Busaniche, Beatriz. Fundación Via Libre; ArgentinaFil: Halvorsen, Alexia. Fundación Via Libre; ArgentinaFil: Bordone, Matías. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Fundación Via Libre; ArgentinaFil: Sanchez, Jorge Adrian. Universidad Nacional de Córdoba. Facultad de Matemática, Astronomía y Física; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin

Mitogen-activated kinase kinase kinase 1 inhibits hedgehog signaling and medulloblastoma growth through GLI1 phosphorylation

The aberrant activation of hedgehog (HH) signaling is a leading cause of the development of medulloblastoma, a pediatric tumor of the cerebellum. The FDA‑approved HH inhibitor, Vismodegib, which targets the transmembrane transducer SMO, has shown limited efficacy in patients with medulloblastoma, due to compensatory mechanisms that maintain an active HH‑GLI signaling status. Thus, the identification of novel actionable mechanisms, directly affecting the activity of the HH‑regulated GLI transcription factors is an important goal for these malignancies. In this study, using gene expression and reporter assays, combined with biochemical and cellular analyses, we demonstrate that mitogen‑activated kinase kinase kinase 1 (MEKK1), the most upstream kinase of the mitogen‑activated protein kinase (MAPK) phosphorylation modules, suppresses HH signaling by associating and phosphorylating GLI1, the most potent HH‑regulated transcription factor. Phosphorylation occurred at multiple residues in the C‑terminal region of GLI1 and was followed by an increased association with the cytoplasmic proteins 14‑3‑3. Of note, the enforced expression of MEKK1 or the exposure of medulloblastoma cells to the MEKK1 activator, Nocodazole, resulted in a marked inhibitory effect on GLI1 activity and tumor cell proliferation and viability. Taken together, the results of this study shed light on a novel regulatory mechanism of HH signaling, with potentially relevant implications in cancer therapy

A methodology to characterize bias and harmful stereotypes in natural language processing in Latin America

Automated decision-making systems, especially those based on natural language processing, are pervasive in our lives. They are not only behind the internet search engines we use daily, but also take more critical roles: selecting candidates for a job, determining suspects of a crime, diagnosing autism and more. Such automated systems make errors, which may be harmful in many ways, be it because of the severity of the consequences (as in health issues) or because of the sheer number of people they affect. When errors made by an automated system affect a population more than others, we call the system \textit{biased}. Most modern natural language technologies are based on artifacts obtained from enormous volumes of text using machine learning, namely language models and word embeddings. Since they are created by applying subsymbolic machine learning, mostly artificial neural networks, they are opaque and practically uninterpretable by direct inspection, thus making it very difficult to audit them. In this paper, we present a methodology that spells out how social scientists, domain experts, and machine learning experts can collaboratively explore biases and harmful stereotypes in word embeddings and large language models. Our methodology is based on the following principles: * focus on the linguistic manifestations of discrimination on word embeddings and language models, not on the mathematical properties of the models * reduce the technical barrier for discrimination experts%, be it social scientists, domain experts or other * characterize through a qualitative exploratory process in addition to a metric-based approach * address mitigation as part of the training process, not as an afterthough