Quantum Intelligence: From Quantum Algorithms to Intelligent Machines

J. Singh, Meenakshi Sharma, Ramendra Pratap Singh +3 more

Nahed Abdelgaber, Chris Nikolopoulos

This paper will review the basic building blocks of quantum computing and discuss the main applications in artificial intelligence that can be addressed more efficiently using the quantum computers of today. Artificial intelligence and quantum computing have many features in common. Quantum computing can provide artificial intelligence and machine learning algorithms with speed of training and computational power in less price. Artificial intelligence on the other hand can provide quantum computers with the necessary error correction algorithms. Some of the algorithms in AI that have been successfully implemented on a quantum computer, that we will present in this paper, are both unsupervised learning algorithms (clustering and Principal component analysis) and also supervised learning classification, such as support vector machines.

Dr. Rakshitha Kiran P Dr. Samitha Khaiyum Dr. Pallavi Kulkarni Dr. Pavithra G. Shetty Prof. Raksha Kodnad R

Abhishek Lohchab, Ashish Kumar Sharma, Yashpal Singh

Achraf Abdelghafour Zemate, Ismail El Gaabouri, Moulay Brahim Sedra

Abstract Artificial intelligence (AI) and quantum computing represent a new frontier in computing, offering the promise of solving problems previously considered unsolvable. This article aims to provide an in-depth analysis of the current state of research at the intersection of quantum computing and artificial intelligence. We will explore the basic concepts, major applications, and challenges of quantum machine learning (QML). We will start by offering a definition of the motivation for the convergence of quantum computing and artificial intelligence. We will then proceed to offer a description of the basic algorithmic frameworks for quantum machine learning, such as quantum differential algorithms (VQA) for near-term quantum (NISQ) devices and the prospects for error-tolerant quantum algorithms such as the Harrow-Hassidim-Lloyd algorithm. We will then proceed to offer a methodical analysis of the current applications of quantum machine learning in finance, cybersecurity, materials science, among other fields. We will then proceed to discuss the basic challenges facing the implementation of quantum machine learning, while offering a glimpse of the future of quantum-enhanced artificial intelligence.

Sayantan Gupta, Subhrodip Mohanta, Mayukh Chakraborty +1 more

Machine Learning or Artificial Intelligence basically involves tasks of modifying and supervising problems taken as vectors in multi-dimensional space. The Primitive algorithms which are used take Polynomial Time for computing such vector problems which are not fruitful for us, on the other hand, Quantum algorithms have the capability to solve such vector problems in a considerable amount of time by using Quantum-Mechanical operations. For example, we can perform a Database Search in a time which is Quadratic-ally faster than the primitive search algorithm. Quantum Algorithms rely on Quantum physics and therefore the algorithms are Incoherent in nature and this property makes them more interesting to study. In this paper, we provide the insights of Quantum Machine Learning and we formally prove that the Execution Time of the algorithm is greatly optimized with the help of Adiabatic Quantum Learning. Also, we prove that Quantum Associative Memories can store exponentially more data than its primitive counterparts. Data mining concept is very similar to Machine Learning and we will also show how QML will be beneficial in such cause as well.