Quantum Enhancements to the AI Industry

Matthias Klusch, Jörg Lässig, Frank K. Wilhelm

Sheik Abdullah Abbas, Parkavi Ravi, S Sujitha

AI with quantum enhancements can solve financial optimization issues more quickly and accurately, potentially revolutionizing financial risk management and investing techniques. Various quantum-enhanced artificial intelligence (AI)-driven models exist that can substantiate the industrial needs of organizations. It fulfills the different measurements encompassing the quantum policy of data-driven approaches. Quantum AI quantum AI , which emerged from the nexus of quantum computing quantum computing and AI, shows great potential for resolving complicated issues previously thought to be unsolvable by conventional computer techniques. We are seeing the rise of practical uses for quantum technologies, which have the potential to transform entire industries and fundamentally alter how we think about computation. This chapter explores different use cases that can aid in the functionality of industrial needs for quantum AI in the decision-making process. The substantiation of the data, models, methods, tools, and applications is the core context of this chapter, which generally aids the evaluation of quantum AI in predicting the pattern behind the data. Use cases focusing on data optimization, drug discovery, molecular simulation, machine acceleration, financial modelling, and secure communication cryptographic schemes will be explored upon the description towards the focus on applications of AI and quantum in practice. The real-world uses of quantum AI are proliferating, providing answers to issues previously thought to be unsolvable by computers. It is anticipated that quantum computing will aid in creating ground-breaking goods and services that will revolutionize and redefine the manufacturing and industrial sectors.

Madhusudan Singh Irish Singh, Bharat S. Rawal

Tobias Stollenwerk, Somtapa Bhattacharya, Michele Cattelan +15 more

The goal of the project \(\mathrm{Q(AI)}^2\) was to acquire a broader basis of quantum computing enhanced AI and optimization algorithms for potential applications in the automotive industry. In particular, we strove to assess the potential for quantum advantage for specific use cases from the automotive industry. For this, we developed quantum algorithmic solutions tailored both to the available hardware as well as to the industrial problems at hand. In addition, we ensured industrial relevance of the investigated applications by the constitution of the consortium, involving all three large German car manufactures VW, Mercedes-Benz and BMW as well as Bosch as a central component supplier. Forschungszentrum Jülich and the German Center for Artificial Intelligence (DFKI) provided the necessary expertise in AI and quantum computing methods.

Pratibha, Pummy Dhiman, Sudha +1 more

Quantum Artificial Intelligence (Quantum AI) is a popular and new computational field that incorporates the use of quantum computing in enhancing Artificial Intelligence (AI). This chapter addresses the use of Quantum AI in three sectors, namely healthcare, finance, and cybersecurity, with a view that the deployment of AI integrated with Quantum computing shall take the respective industry to another level. Quantum AI enhances diagnosis and medical imaging, pattern recognition, and diagnostics, which increases the rate of accuracy or detection and diagnosis of diseases and illnesses. Moreover, Quantum AI optimizes portfolio management, risk evaluation, and prediction, and fraud detection in the financial industry, as well as enhances the computational rate in comparison to current rates. Quantum is used to enhance investment solutions and approaches to assess large amounts of big data as soon as possible, resulting in more potent risk management tools. Also, they improve the detection of fraud in financial transactions by providing a means of detecting the variation in such transactions, making security in online/mobile banking and other facets of electronic payment firmer. On the same level, they defend computer networks and systems from cyber-attacks through massive and complicated structures of analyzing large data sets in an incredibly short period. Technology has certain drawbacks, which include issues in quantum computer hardware, errors that could be present in quantum structures, and moral issues relating to the safety of data and information. A solution to these challenges could only be good if Quantum AI becomes widely implemented across the numerous domains of industry today. This Chapter gives an elaborate overview of the relevance of Quantum AI in healthcare, finance, and cybersecurity, which defines the trend of intelligent and secure computing systems.