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.
Top1. Introduction
Artificial intelligence has achieved remarkable success in recent years, enabled by increasing computational power, large datasets, and algorithmic advances (Jordan and Mitchell, 2015). However, fundamental limitations remain in mainstream AI techniques. Deep neural networks rely heavily on vast training data, and suffer performance degradation when applied to distributions differing from the training set, as seen in failures like Microsoft's Tay chatbot which was manipulated into making offensive statements on Twitter (Kiela et al., 2021). Lack of model interpretability creates trust issues for high-stakes applications like healthcare (Tonekaboni et al., 2019), where opaque predictions can potentially cause harm. Combinatorial optimization tasks like protein folding become intractable as problem sizes grow (Perdomo-Ortiz et al., 2015), limiting the ability to model complex biomolecular structures.
Meanwhile, quantum computing has emerged as a revolutionary computational paradigm introducing new physics-inspired approaches that classical systems fundamentally cannot replicate (Dowling and Milburn, 2003). By leveraging uniquely quantum properties like superposition, entanglement, and interference, quantum processors operate in ways drastically different from classical machines. For instance, superposition enables intrinsic parallelism where one operation evaluates multiple states simultaneously. Entanglement can represent correlated datasets unavailable to classical systems. Cleverly designed quantum algorithms like Grover's search (Grover, 1996) and Shor's factoring (Shor, 1997) demonstrate quadratic or exponential speedups over the best known classical techniques for certain problems (Khang & Kali, 2024).
Recent rapid advances in quantum processor engineering, algorithms, and software tools have brought this disruptive technology closer to delivering practical applications (Mohseni et al., 2017). Accessible via cloud services, noisy intermediate-scale quantum (NISQ) processors with 50-100 qubits are already being applied to specialized tasks like quantum chemistry simulations (Cao et al., 2019a), finance (Orús et al., 2019), and optimization (Neukart et al., 2017). The prospect of scaled-up fault-tolerant quantum computers emerging in the next 10-15 years has sparked tremendous excitement about the monumental impact across industries (Prasad et al., 2021).
One of the most promising application domains is augmenting artificial intelligence. The combination of quantum computing and AI offers new avenues for processing massive data sets, achieving speedups on constrained problems, modeling quantum systems, and enhancing deep learning techniques. Realizing this potential requires collaborative expertise spanning physics, computer science, and mathematics, engineering, and domain sciences. While reaching practical quantum-enhanced AI still faces multiple challenges, steady progress on the hardware and algorithm fronts aims to unlock the joint benefits (Shyam &Khang et al., 2023). This chapter provides a comprehensive analysis of the emerging interplay between quantum computing and artificial intelligence. The key topics covered are:
- 1.
Fundamentals of quantum computing formalism and qubit implementations
- 2.
Quantum algorithms and software frameworks
- 3.
Quantum improvements for optimization, sampling, and simulation
- 4.
Quantum neural networks, quantum deep learning, and quantum machine learning
- 5.
Engineering challenges in scaling up quantum systems
- 6.
Promising application areas and real-world demonstrations
- 7.
Projections and timelines forecasting the future of quantum AI