Ebook - IA Quantique [FR]

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Posts les plus consultés de ce blog

Quantum AI - Part 3 - The rise of the AIs

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Previously, in the Quantum AI column, we have seen that quantum computing would theoretically be able to significantly increase the execution speed of some Machine Learning algorithms. This is due to the phenomena at the core of such computers (like states superposition, qubits entanglement, superposed states decoherence, immediate wave function collapse) that enable parallel calculations by storing several (superposed) information within a single particle. By considerably speeding up algorithmic processing, quantum will make AI solve new problems. Some people go even further and say it could enable the design of an artificial brain as efficient as that of humans, especially able to integrate abstract concepts such as consciousness and feelings. The question that then arises is whether quantum AI would be more likely to become “strong AI” (artificial general intelligence)… First of all, what is human intelligence? Before looking at the complementarities between quantum and strong ...
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Quantum AI - Part 2 - The dice have been cast!

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Previously, in the Quantum AI column, we have seen that quantum computing would theoretically enable some complex machine learning algorithms to be executed in a “reasonable” time (less than several years…). But what is so different about quantum computers compared to today’s computers? The purpose of this article is not to go into theoretical details but to simply illustrate – as faithfully as possible – the fundamental differences between classical and quantum computers, especially with a quantum algorithm example. Understanding the bit concept with the coin analogy To understand why calculations are potentially much faster in a quantum computer, let’s recall the general functioning of conventional computers. They use bits to code information and are made of electronic circuits so that, when the current flows through the circuit, the bit is worth 1, otherwise the bit is worth 0. You can also consider the electrical voltage rather than the current, the approach will remain the...
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IA Quantique - Partie 4 - Le rôle clé du DataOps

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Cet article est le quatrième d’une chronique sur l’IA quantique. Précédemment, on a pu voir que : Partie 1 : l’informatique quantique permettra d’accélérer sensiblement l’exécution de certains algorithmes de machine learning et de traitements cryptographiques ; Partie 2 : les phénomènes quantiques (superposition et intrication) responsables de la parallélisation intrinsèque des calculs ne peuvent être exploités que pendant une très courte période dans des conditions d’isolation strictes (problème de décohérence) ; Partie 3 : malgré toutes ses promesses, l’IA quantique ne sera probablement pas en mesure de donner naissance à une conscience artificielle ou une prétendue IA forte ; Dans ce nouveau chapitre, je souhaite vous partager mon point de vue sur la mise en oeuvre théorique d’un dispositif quantique de production en entreprise. Cela peut par exemple être un processus décisionnel (BI) ou de machine learning reposant sur une infrastructure composée à la fois de machines c...
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