<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Adel Sohbi | LIP6 - Équipe QI</title><link>https://qi.lip6.fr/fr/people/adel-sohbi/</link><atom:link href="https://qi.lip6.fr/fr/people/adel-sohbi/index.xml" rel="self" type="application/rss+xml"/><description>Adel Sohbi</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>fr</language><copyright>© 2022 LIP6 Quantum Information Team</copyright><lastBuildDate>Mon, 30 Oct 2023 00:00:00 +0000</lastBuildDate><image><url>https://qi.lip6.fr/media/icon_hu_bdeccd9e706ea09d.png</url><title>Adel Sohbi</title><link>https://qi.lip6.fr/fr/people/adel-sohbi/</link></image><item><title>Corrected Bell and Noncontextuality Inequalities for Realistic Experiments</title><link>https://qi.lip6.fr/fr/publication/4271961-corrected-bell-and-noncontextuality-inequalities-for-realistic-experiments/</link><pubDate>Mon, 30 Oct 2023 00:00:00 +0000</pubDate><guid>https://qi.lip6.fr/fr/publication/4271961-corrected-bell-and-noncontextuality-inequalities-for-realistic-experiments/</guid><description>&lt;p&gt;Contextuality is a feature of quantum correlations. It is crucial from a foundational perspective as a nonclassical phenomenon, and from an applied perspective as a resource for quantum advantage. It is commonly defined in terms of hidden variables, for which it forces a contradiction with the assumptions of parameter-independence and determinism. The former can be justified by the empirical property of non-signalling or non-disturbance, and the latter by the empirical property of measurement sharpness. However, in realistic experiments neither empirical property holds exactly, which leads to possible objections to contextuality as a form of nonclassicality, and potential vulnerabilities for supposed quantum advantages. We introduce measures to quantify both properties, and introduce quantified relaxations of the corresponding assumptions. We prove the continuity of a known measure of contextuality, the contextual fraction, which ensures its robustness to noise. We then bound the extent to which these relaxations can account for contextuality, via corrections terms to the contextual fraction (or to any noncontextuality inequality), culminating in a notion of genuine contextuality, which is robust to experimental imperfections. We then show that our result is general enough to apply or relate to a variety of established results and experimental setups.&lt;/p&gt;</description></item><item><title>Quantum machine learning with adaptive linear optics</title><link>https://qi.lip6.fr/fr/publication/3138156-quantum-machine-learning-with-adaptive-linear-optics/</link><pubDate>Mon, 05 Jul 2021 00:00:00 +0000</pubDate><guid>https://qi.lip6.fr/fr/publication/3138156-quantum-machine-learning-with-adaptive-linear-optics/</guid><description>&lt;p&gt;We study supervised learning algorithms in which a quantum device is used to perform a computational subroutine - either for prediction via probability estimation, or to compute a kernel via estimation of quantum states overlap. We design implementations of these quantum subroutines using Boson Sampling architectures in linear optics, supplemented by adaptive measurements. We then challenge these quantum algorithms by deriving classical simulation algorithms for the tasks of output probability estimation and overlap estimation. We obtain different classical simulability regimes for these two computational tasks in terms of the number of adaptive measurements and input photons. In both cases, our results set explicit limits to the range of parameters for which a quantum advantage can be envisaged with adaptive linear optics compared to classical machine learning algorithms: we show that the number of input photons and the number of adaptive measurements cannot be simultaneously small compared to the number of modes. Interestingly, our analysis leaves open the possibility of a near-term quantum advantage with a single adaptive measurement.&lt;/p&gt;</description></item><item><title>Quantum machine learning with adaptive linear optics</title><link>https://qi.lip6.fr/fr/publication/4990670-quantum-machine-learning-with-adaptive-linear-optics/</link><pubDate>Mon, 05 Jul 2021 00:00:00 +0000</pubDate><guid>https://qi.lip6.fr/fr/publication/4990670-quantum-machine-learning-with-adaptive-linear-optics/</guid><description>&lt;p&gt;We study supervised learning algorithms in which a quantum device is used to perform a computational subroutine - either for prediction via probability estimation, or to compute a kernel via estimation of quantum states overlap. We design implementations of these quantum subroutines using Boson Sampling architectures in linear optics, supplemented by adaptive measurements. We then challenge these quantum algorithms by deriving classical simulation algorithms for the tasks of output probability estimation and overlap estimation. We obtain different classical simulability regimes for these two computational tasks in terms of the number of adaptive measurements and input photons. In both cases, our results set explicit limits to the range of parameters for which a quantum advantage can be envisaged with adaptive linear optics compared to classical machine learning algorithms: we show that the number of input photons and the number of adaptive measurements cannot be simultaneously small compared to the number of modes. Interestingly, our analysis leaves open the possibility of a near-term quantum advantage with a single adaptive measurement.&lt;/p&gt;</description></item><item><title>Experimental Approach to Demonstrating Contextuality for Qudits</title><link>https://qi.lip6.fr/fr/publication/3093475-experimental-approach-to-demonstrating-contextuality-for-qudits/</link><pubDate>Wed, 23 Jun 2021 00:00:00 +0000</pubDate><guid>https://qi.lip6.fr/fr/publication/3093475-experimental-approach-to-demonstrating-contextuality-for-qudits/</guid><description>&lt;p&gt;We propose a method to experimentally demonstrate contextuality with a family of tests for qudits. The experiment we propose uses a qudit encoded in the path of a single photon and its temporal degrees of freedom. We consider the impact of noise on the effectiveness of these tests, taking the approach of ontologically faithful non-contextuality. In this approach, imperfections in the experimental set up must be taken into account in any faithful ontological (classical) model, which limits how much the statistics can deviate within different contexts. In this way we bound the precision of the experimental setup under which ontologically faithful non-contextual models can be refuted. We further consider the noise tolerance through different types of decoherence models on different types of encodings of qudits. We quantify the effect of the decoherence on the required precision for the experimental setup in order to demonstrate contextuality in this broader sense.&lt;/p&gt;</description></item><item><title>Certifying dimension of quantum systems by sequential projective measurements</title><link>https://qi.lip6.fr/fr/publication/3270658-certifying-dimension-of-quantum-systems-by-sequential-projective-measurements/</link><pubDate>Thu, 10 Jun 2021 00:00:00 +0000</pubDate><guid>https://qi.lip6.fr/fr/publication/3270658-certifying-dimension-of-quantum-systems-by-sequential-projective-measurements/</guid><description>&lt;p&gt;This work analyzes correlations arising from quantum systems subject to sequential projective measurements to certify that the system in question has a quantum dimension greater than some d. We refine previous known methods and show that dimension greater than two can be certified in scenarios which are considerably simpler than the ones presented before and, for the first time in this sequential projective scenario, we certify quantum systems with dimension strictly greater than three. We also perform a systematic numerical analysis in terms of robustness and conclude that performing random projective measurements on random pure qutrit states allows a robust certification of quantum dimensions with very high probability.&lt;/p&gt;</description></item></channel></rss>