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Quantum machine learning: quantum algorithms and neural networks
Le D., Song H., Vyas N., Raj P., DE GRUYTER, Berlin, Germany, 2024. 334 pp. Type: Book (9783111342092)
Date Reviewed: Jan 20 2025

Writing a book about a rapidly evolving technology like quantum computing (QC) is no easy task, especially in light of its potential impact on other rapidly developing fields such as deep machine learning, reinforcement learning (RL), natural language processing (NLP), blockchain, renewable energy systems, drug development, and more. The challenge was compounded by the authors’ and editors’ decision to avoid using mathematics or in-depth discussions about complex concepts, resulting in a collection of high-level papers written by multiple contributors. This structure required reintroducing the same foundational ideas repeatedly throughout the book, making it a somewhat disjointed and difficult read.

Each chapter concludes with an extensive list of references, which could be a valuable resource for researchers. The book’s focus on the applications of QC--rather than the technical details of how QC works--may also appeal to some researchers.

Chapter 12 discusses interesting software development and debugging tools, such as IBM’s Qiskit, for quantum programming. For newcomers to QC, the book introduces high-level ideas like variational quantum circuits (chapter 11), quantum face recognition (chapter 14), and quantum drug discovery (chapter 3). However, more comprehensive and accessible resources on these topics are available online [1,2]. The same applies to foundational algorithms like Shor’s factorization algorithm [3], Grover’s search algorithm [4], and quantum error correction methods [5].

The book includes numerous figures, but some are not well explained. For example, Figures 1.2 and 14.2 could benefit from additional context to enhance their usefulness. Similarly, the text contains several editorial oversights. For instance, on page 29, the term “quantum machine learning” is introduced, but the section instead describes quantum teleportation. Chapters 5 and 7, which cover blockchain and renewable energy, seem disconnected from the book’s machine learning focus. Chapter 13, “Quantum-Enhanced Neural Networks,” does not address neural networks or backpropagation. Additionally, on page 307, Figure 14.4 is incorrectly referred to as Figure 14.5, with no discussion of the latter, pointing to the need for more thorough editing.

Some sections introduce intriguing ideas but lack depth or substantiation. For example, Section 9.6.7 (page 182) presents the quantum Bellman equation without proof or references, leaving readers to question its validity.

In summary, while the book offers glimpses into the potential applications of quantum computing, it often leaves readers wishing for greater clarity, detail, and cohesion.

Reviewer:  Subhankar Ray Review #: CR147872
1) Salari, V.; Paneru, D.; Saglamyurek, E.; Ghadimi, M.; Abdar, M.; Rezaee, M.; Aslani, M.; Barzanjeh, S.; Karimi, E. Quantum face recognition protocol with ghost imaging. Scientific Reports 13, (2023), Article number: 2401 2023.
2) Newton, W. Quantum medicine: how quantum computers could change drug development. Clinical Trials Arena (February 24, 2023), https://www.clinicaltrialsarena.com/features/quantum-computers-drug-development/.
3) Shor, P. W. Polynomial-time algorithms for prime factorization and discrete logarithms on a quantum computer. SIAM Journal on Computing 26, (1997), 1484–1509.
4) Microsoft Learn. Theory of Grover's search algorithm. (January 16, 2025), https://learn.microsoft.com/en-us/azure/quantum/concepts-grovers.
5) Bausch, J.; Senior, A. W.; Heras, F. J. H.; Edlich, T.; Davies, A.; Newman, M.; Jones, C.; Satzinger, K.; Niu, M. Y.; Blackwell, S.; Holland, G.; Kafri, D.; Atalaya, J.; Gidney, C.; Hassabis, D.; Boixo, S.; Neven, H.; Kohli, P. Learning high-accuracy error decoding for quantum processors. Nature 635, (2024), 834–840.
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