Quantum AI (QAI) at the Quantum Computing Lab

Created: Damir Cavar, 2026-10-02

Last change: Damir Cavar, 2026-10-02

Books

• Bernhardt, Chris (2020) Quantum Computing for Everyone. The MIT Press.
• Coecke, Bob, and Stefano Gogioso (2023) Quantum in Pictures: A New Way to Understand the Quantum World, Quantinuum.
• Coecke, Bob, and Aleks Kissinger (2017) Picturing Quantum Processes: A First Course in Quantum Theory and Diagrammatic Reasoning, Cambridge University Press.
• Combarro, Elías F., and Samuel González-Castillo (2023) A Practical Guide to Quantum Machine Learning and Quantum Optimization. Packt Publishing. (GitHub repo)
• Freericks, James K. (2026) Quantum Mechanics Done Right: The Shortest Path from Novice to Researcher. Springer Cham.
• Hughes, Ciaran, et al. (2021) Quantum Computing for the Quantum Curious. Springer. Open Access, free download.
• Johnston, Eric R., Nic Harrigan, Mercedes Gimeno-Segovia (2019) Programming Quantum Computers. O’Reilly Media, Inc.
• Luongo, Alessandro (2023) Quantum algorithms for data analysis.
• Nielsen, Michael A., and Isaac L. Chuang. Quantum Computation and Quantum Information: 10th Anniversary Edition. Cambridge: Cambridge University Press, 2010.
• Pastorello, Davide (2023) Concise Guide to Quantum Machine Learning. Springer.
• Rudolph, Terry (2017) Q is for Quantum: Quantum mechanics for those who only know basic arithmetic.
• Schuld, Maria, and Francesco Petruccione (2021) Machine Learning with Quantum Computers. Springer.
• Sutor, Robert S. (2019) Dancing with Qubits: How quantum computing works and how it can change the world. Packt Publishing. (GitHub repo)
• Watrous, John (2025) Understanding Quantum Information and Computation. arXiv:2507.11536.
• Weaver, James L., Frank J. Harkins (2022) Qiskit Pocket Guide. O’Reilly Media, Inc. (GitHub repo)
• Woody III, Leonard S. (2022) Essential Mathematics for Quantum Computing. Packt Publishing.

Relevant literature from Cognitive Science and Psychology:

• Jerome R. Busemeyer and Peter D. Bruza (2012) Quantum Models of Cognition and Decision. Cambridge University Press. (FYI: the second edition is coming out soon and it has a special chapter on Large Language Models!)

Papers

• Bergholm, Ville, Josh Izaac, Maria Schuld, et al. (2022) “PennyLane: Automatic Differentiation of Hybrid Quantum-Classical Computations.” arXiv:1811.04968. Preprint, arXiv: 10.48550/arXiv.1811.04968.
• Dawid, Anna, Julian Arnold, Borja Requena, et al. (2025) Modern Applications of Machine Learning in Quantum Sciences. doi.
• Laakkonen, Konstantinos, and Coecke (2024) Quantum Algorithms for Compositional Text Processing.
• Zhang et al. (2023) Applications of Quantum Embedding in Computer Vision (focus on Quantum Embeddings)
• Schuld et al. (2014) An introduction to quantum machine learning.
• Coecke et al. (2010) Mathematical Foundations for a Compositional Distributional Model of Meaning.
• Coecke (2019) The Mathematics of Text Structure.
• Gogioso (2016) A Corpus-based Toy Model for DisCoCat.
• Bradley et al. (2018) Translating and Evolving: Towards a Model of Language Change in DisCoCat.
• Kartasaklis et al. (2021) lambeq: An Efficient High-Level Python Library for Quantum NLP.
• Widdows et al. (2022) Near-Term Advances in Quantum Natural Language Processing.
• Alexander and Widdows (2023) Quantum Text Encoding for Classification Tasks.
• Lorenz et al. (2021) QNLP in Practice: Running Compositional Models of Meaning on a Quantum Computer.
• Meichanetzidis et al. (2020) Grammar-aware sentence classification on quantum computers.
• Bob Coecke (2023) Our quest for finding the universality of language
• Widdows, Dominic and Stanley Peters (2003) Word Vectors and Quantum Logic Experiments with negation and disjunction.