IA Quantique | Quantum AI

This article is the fourth of our quantum AI column. Previously, we have seen that:  Chapter 1: quantum computing will significantly accelerate the execution of some machine learning algorithms and cryptographic processing; Chapter 2: quantum phenomena (superposition and entanglement) responsible for parallel computing can only be exploited for a very short time under strict isolation conditions (decoherence problem); Chapter 3: Despite all its promises, quantum AI will probably not be able to give birth to an artificial consciousness or a so-called strong AI; In this new chapter, I want to share my point of view on the theoretical implementation of a quantum production device in a company. This can for example be a decision-making (BI) or machine learning process based on an infrastructure mixing both conventional and quantum machines. The interest being, of course, to take advantage of quantum processors to solve problems that are insoluble today, regardless of the servers us

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

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 th

"AI" and "quantum". Here you go, two buzzwords for the price of one! More seriously, although I am aware that it is difficult to sum up two such big fields with a few articles, I wanted to try to expose, as faithfully as possible, the pros and cons of quantum computing applied to artificial intelligence, and more specifically to machine learning. The main underlying problem is the limitation of our computing capabilities to execute heavy algorithms. Indeed, although the power of our equipment has increased by 10 times in the last thirty years, we must keep in mind that we will always need more resources and that traditional computing won’t allow us to tackle some big data and IoT issues. Quantum mechanics might be one of the technical solutions to make computer science enter a new era in terms of security and algorithms execution speed. Did you say "quantum"? First, we could recall where the adjective "quantum" comes from. This term is so ov

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

Précédemment, dans l’IA Quantique, on a vu que l’informatique quantique serait en principe capable d’accroître sensiblement la vitesse d’exécution de certains algorithmes de Machine Learning. Cela est dû au fait que certains phénomènes siégeant au coeur de tels ordinateurs (superposition des états, intrication des qubits, décohérence des états superposés, réduction brutale du paquet d’ondes) permettent de paralléliser les calculs en stockant plusieurs informations (superposées) avec une même particule. En accélérant considérablement certains traitements algorithmiques, on comprend que la quantique permettra aux IA de résoudre de nouveaux problèmes. Certains vont plus loin encore et y voient même un potentiel pour rendre un cerveau artificiel aussi performant que celui des humains, capable notamment d’intégrer des concepts abstraits comme la conscience et les sentiments. La question qui se pose alors est de savoir si l’IA quantique serait plus susceptible de devenir une “IA forte”… D