Realizing Quantum AI Projects

Dominic Widdows, Kirsty Kitto, Trevor Cohen

In the decade since 2010, successes in artificial intelligence have been at the forefront of computer science and technology, and vector space models have solidified a position at the forefront of artificial intelligence. At the same time, quantum computers have become much more powerful, and announcements of major advances are frequently in the news. The mathematical techniques underlying both these areas have more in common than is sometimes realized. Vector spaces took a position at the axiomatic heart of quantum mechanics in the 1930s, and this adoption was a key motivation for the derivation of logic and probability from the linear geometry of vector spaces. Quantum interactions between particles are modelled using the tensor product, which is also used to express objects and operations in artificial neural networks. This paper describes some of these common mathematical areas, including examples of how they are used in artificial intelligence (AI), particularly in automated reasoning and natural language processing (NLP). Techniques discussed include vector spaces, scalar products, subspaces and implication, orthogonal projection and negation, dual vectors, density matrices, positive operators, and tensor products. Application areas include information retrieval, categorization and implication, modelling word-senses and disambiguation, inference in knowledge bases, decision making, and and semantic composition. Some of these approaches can potentially be implemented on quantum hardware. Many of the practical steps in this implementation are in early stages, and some are already realized. Explaining some of the common mathematical tools can help researchers in both AI and quantum computing further exploit these overlaps, recognizing and exploring new directions along the way.This paper describes some of these common mathematical areas, including examples of how they are used in artificial intelligence (AI), particularly in automated reasoning and natural language processing (NLP). Techniques discussed include vector spaces, scalar products, subspaces and implication, orthogonal projection and negation, dual vectors, density matrices, positive operators, and tensor products. Application areas include information retrieval, categorization and implication, modelling word-senses and disambiguation, inference in knowledge bases, and semantic composition. Some of these approaches can potentially be implemented on quantum hardware. Many of the practical steps in this implementation are in early stages, and some are already realized. Explaining some of the common mathematical tools can help researchers in both AI and quantum computing further exploit these overlaps, recognizing and exploring new directions along the way.

Avik Mahata

Ushaa Eswaran, Alex Khang, Vishal Eswaran

Quantum computing introduces revolutionary techniques for information processing by harnessing uniquely quantum phenomena. The emergence of quantum processors, algorithms, and software tools has brought this disruptive technology closer to practical applications. One of the most promising domains where quantum techniques can provide advantages is augmenting artificial intelligence. This chapter provides a comprehensive analysis of the fundamentals underpinning quantum computation, surveys the current hardware landscape, summarizes key quantum algorithms, and highlights promising applications in optimization, sampling, simulation, and machine learning. Engineering challenges in scaling up quantum systems are discussed along with progress in quantum error correction. Projections forecast steady advances towards quantum-enhanced AI based on a timeline of milestones. Realizing these synergies through collaborative interdisciplinary efforts promises to transform computing and catalyse innovations across industries to benefit science and society.

Kaoru Aguilera Katayama

Matthias Klusch, Jörg Lässig, Frank K. Wilhelm

Vikram Simha Reddy, R. Mythili, Aditya Swaroop +1 more

The objective of this project aims to develop a Personal AI Travel Assistant using Bot press, a powerful opensource conversational AI framework. a robust open-source natural language processing framework—and the potential of quantum computing, this project seeks to construct a Personal AI Travel Assistant. The Personal AI Travel Companion is at the forefront of innovation when it comes to personal trip organizing and handling thanks to the use of quantum computing. The assistant can handle massive volumes of data and execute complicated computations with lightning speed and efficiency thanks to quantum computing. Personalized trip suggestions, itinerary its creation, real-time travel updates, and seamless help are just a few of the services that consumers can expect from this quantum-enhanced personalized AI travel assistant.