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Amsterdam University Press eBooks

9 Optimisation and AI: What are companies doing today?

Koen Groenland

Published Mar 9, 2025• 0 citations
Publisher

Abstract

Optimisation and AI: What are companies doing today?The earlier chapter on quantum applications discussed whether quantum computers can offer practical speedups for optimisation and AI.We concluded that this is a subtle case and that it is still unclear how much utility quantum computers can provide in these fields.Still, a large body of literature claims some form of near-term speedup in specific quantum applications.What's really going on here?This chapter aims to build a more detailed intuition of how different organisations are exploring quantum applications.We will assess some example research papers, examine the problems they tackle, and analyse how convincingly they point to quantum utility.Moreover, we will look at the most fruitful directions for finding new and useful quantum algorithms.But before we dive into these details, let's take a step back and ask ourselves: what must a quantum algorithm do to be a genuine improvement over its classical counterpart?

DOI
10.1515/9789048568994-010

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Optimisation and AI: What are companies doing today?The earlier chapter on quantum applications discussed whether quantum computers can offer practical speedups for optimisation and AI.We concluded that this is a subtle case and that it is still unclear how much utility quantum computers can provide in these fields.Still, a large body of literature claims some form of near-term speedup in specific quantum applications.What's really going on here?This chapter aims to build a more detailed intuition of how different organisations are exploring quantum applications.We will assess some example research papers, examine the problems they tackle, and analyse how convincingly they point to quantum utility.Moreover, we will look at the most fruitful directions for finding new and useful quantum algorithms.But before we dive into these details, let's take a step back and ask ourselves: what must a quantum algorithm do to be a genuine improvement over its classical counterpart?
The field of quantum computing is presently undergoing a significant transition. Even if quantum computers are now so large and capable that they can only be used under certain limitations, be conventionally emulated, it is still unknown which applications can benefit from a quantum advantage. With an emphasis on artificial intelligence (AI) and its applications as some of the most significant key technologies of our time, this provided an excellent starting point for looking beyond the already demonstrated acceleration of a few specific classical computing tasks by quantum computers, such as unstructured search and prime factorization. Actually, the goal of Quantum Artificial Intelligence (QAI) is to merge the two worlds for each’s benefit. Stated differently, QAI research focuses on using quantum computing to solve computationally challenging AI problems and, conversely, using AI to address the difficulties associated with developing and running quantum computing systems. The creation of direct quantum or hybrid quantum-classical algorithms for problem solving and the examination of their possible benefits over the best classical alternatives are of particular interest.
Quantum computing offers a fundamentally new paradigm for solving complex optimisation and learning tasks by exploiting superposition, entanglement, and other quantum phenomena. Over the past decade, research has increasingly focused on quantum-enhanced approaches to optimisation and machine learning (ML), showing the potential to outperform classical methods in specific settings (Zaman, 2023; Peral-García, 2024). This paper presents a comprehensive review of quantum algorithms for optimisation (e.g., the Quantum Approximate Optimization Algorithm and quantum annealing) and quantum machine learning frameworks (such as quantum support vector machines and quantum neural networks), analysing their theoretical underpinnings, implementation status, and applicability to real-world tasks. Key challenges including hardware noise, limited qubit counts, and algorithmic scalability are examined (Chen, 2024). We also explore hybrid quantum-classical architectures as a near-term route to quantum advantage and propose future research directions aimed at bridging the current gap between quantum algorithm theory and large-scale deployment. Through this synthesis, we aim to provide both academic and practitioner audiences with a clear roadmap for leveraging quantum algorithms in optimisation and ML workflows
Abstract Quantum computing is gaining popularity across a wide range of scientific disciplines due to its potential to solve long-standing computational problems that are considered intractable with classical computers. One promising area where quantum computing has potential is in the speed-up of NP -hard optimisation problems that are common in industrial areas such as logistics and finance. Newcomers to the field of quantum computing who are interested in using this technology to solve optimisation problems do not have an easily accessible source of information on the current capabilities of quantum computers and algorithms. This paper aims to provide a comprehensive overview of the theory of quantum optimisation techniques and their practical application, focusing on their near-term potential for noisy intermediate scale quantum devices. The paper starts by drawing parallels between classical and quantum optimisation problems, highlighting their conceptual similarities and differences. Two main paradigms for quantum hardware are then discussed: analogue and gate-based quantum computers. While analog devices such as quantum annealers are effective for some optimisation problems, they have limitations and cannot be used for universal quantum computation. In contrast, gate-based quantum computers offer the potential for universal quantum computation, but they face challenges with hardware limitations and accurate gate implementation. The paper provides a detailed mathematical discussion with references to key works in the field, as well as a more practical discussion with relevant examples. The most popular techniques for quantum optimisation on gate-based quantum computers, the quantum approximate optimisation algorithm and the quantum alternating operator ansatz framework, are discussed in detail. However, it is still unclear whether these techniques will yield quantum advantage, even with advancements in hardware and noise reduction. The paper concludes with a discussion of the challenges facing quantum optimisation techniques and the need for further research and development to identify new, effective methods for achieving quantum advantage.
Quantum computing and quantum machines continue to raise awareness once people realized that “Nature isn’t classical. If you want to make a simulation of nature, you’d better make it quantum mechanical.” ( Feynman, 1982 ). On the other hand, as the Internet brings big data, machine learning has become a logical step for researchers looking for tasks such as the patterns behind the data, or for companies seeking to maximize their data’s potential value. The combination of the quantum computing and machine learning (ML) fields has resulted in quantum machine learning (QML), one of today’s most active fields. This preview provides essential concepts in quantum computation, including the basic quantum physics and fundamentals of quantum computing. The next section examines quantum-enhanced ML models and algorithms. The current state of knowledge of applications is discussed in the following section and the preview concludes with open questions for future research on QML.
Abstract Optimisation appears in the workflow of virtually every application and is often a computationally expensive step. Therefore, exploring whether quantum computing can offer an advantage in optimisation – even if only in certain circumstances – is extremely well-motivated from a practical perspective. Unlike the other algorithms covered in the book, which are expressed and analysed as quantum circuits, the most natural starting point for understanding quantum optimisation is the adiabatic model of quantum computation, which is introduced in this chapter. The quantum approximate optimisation algorithm, a gate-based algorithm inspired by quantum annealing (itself closely-related to adiabatic quantum computation), is also covered.