Quantum technologies propelling AI Conversely, quantum computing has the potential to revolutionize AI by overcoming the computational limitations of classical systems. Quantum computers could increase the speed of training AI models, for instance in deep learning, where quantum sampling (Quantum Monte Carlo) techniques may significantly speed up the training process. (This speed-up is quadratic, meaning that the improvement in training time scales with the square of the problem size, leading to faster and more accurate model development.) Quantum mechanics also inspires new types of ML algorithms, such as quantum reservoir computing.27 These algorithms leverage the unique properties of quantum systems that enable them to perform computations that are infeasible for classical computers. For instance, quantum classification algorithms can enable more efficient data segmentation, enhancing the performance of recommendation engines. Researchers are exploring the results of implementing classically intractable ML algorithms on quantum computers to potentially provide more accurate and personalized recommendations. 28 Moreover, quantum computing has the potential to improve image recognition. This is because quantum algorithms may be able to handle the high-dimensional data involved in image recognition tasks more effectively than classical algorithms, leading to more accurate and reliable recognition systems. This advancement has significant implications for fields such as medical imaging, autonomous vehicles and security. Quantum machine learning (QML): The future of AI algorithms This is one of the most promising areas where AI and quantum computing reinforce each other. In this longer-term vision, highly trained AI platforms could have access to additional inferential power (thus enhancing their performance) by leveraging quantum computers as problem- or data-specific accelerators. Quantum computers’ ability to process and evaluate data in ways that are intractable on classical computers opens new frontiers for ML. Complex tasks such as pattern recognition, optimization problems and data analysis could be aided by future quantum machines when QML proves scalable. This paradigm shift promises to enhance the capabilities of AI, enabling breakthroughs in fields ranging from natural language processing to drug discovery. AI-enhanced quantum technologies: Current practical applications Another critical area of synergy is the enhancement of today’s quantum technologies through AI. For example, reducing the size of quantum sensors – by eliminating the need for bulky Faraday cages or cryogenic cooling – requires sophisticated ML algorithms for signal processing. AI plays a crucial role in making quantum sensors more practical and efficient by processing the raw data they collect into usable results. This integration is already being applied in quantum navigation systems and quantum medical devices, where the powerful ML layer ensures accurate and reliable performance in real-world applications. AI’s broader ML capabilities, beyond just generative AI or large language models (LLMs), are essential in this context. Techniques such as neural networks, reinforcement learning and unsupervised learning are crucial for developing and refining quantum technologies. These advancements make quantum sensors more accessible and versatile, paving the way for their use in various industries, from healthcare to aerospace. The future of AI and quantum computing Despite the promising advancements, there are still challenges (Section 1.2) to overcome. Currently, generative AI (GenAI) struggles with complex mathematical tasks, particularly in converting classical algorithms to quantum algorithms. However, as AI continues to develop and improve its understanding of mathematical libraries and solvers, it holds the potential to bridge this gap. Business leaders preparing for this future of AI and quantum computing should consider the strategic value of large quantitative models (LQMs), which use advanced algorithms to analyse and simulate complex systems, providing deep insights in various industries. These models, which run on classical not quantum computers, can simulate quantum mechanical behaviours and other quantitative factors that traditional methods struggle to address. SandboxAQ CEO Jack Hidary emphasizes the importance of this convergence: “Advanced quantum technologies paired with powerful Large Quantitative Models – or LQMs – are already delivering significant value across industries such as aerospace, biopharma, healthcare, chemicals, manufacturing, defence, finance and other sectors years before quantum computers become mainstream.” As AI and quantum computing technologies continue to evolve, their convergence is set to create new fields of research and application, enabling quantum-enhanced AI systems to solve problems currently beyond reach, such as optimizing complex logistics networks and discovering new materials and drugs. This symbiotic relationship holds immense potential, promising to revolutionize industries, drive innovation and address some of humanity’s most complex challenges. Embracing the Quantum Economy: A Pathway for Business Leaders 15

Embracing the Quantum Economy 2024