Prof. Dr. Christian Bauckhage, co-director of the Lamarr Institute, and Prof. Dr. Rafet Sifa, principal investigator at the Lamarr Institute, have published a new open-access textbook entitled “Quantum Computing from Hopfield Nets – A Textbook with Python Code Examples,” which offers an unusual but extremely accessible introduction to the world of quantum computing.
Instead of starting directly with abstract concepts such as qubits, Hilbert spaces, or Dirac notation, the textbook provides an introduction using familiar AI models. Hopfield nets serve as a starting point because their functioning has many structural parallels to quantum mechanics, making the transition to the more complex theory more intuitive. “Many people find quantum computing abstract or difficult to grasp. It was important to us to show that there are other ways to approach the subject—namely, using tools that students are already familiar with,” explains Prof. Christian Bauckhage. “When you understand how closely certain AI models and quantum mechanical descriptions are related, the field becomes much more tangible – and it becomes clear why quantum computing opens up new perspectives in specific problem classes.”
The textbook is based on many years of teaching and research experience and offers a practical approach: in addition to theoretical fundamentals, it contains numerous Python, NumPy, and SciPy examples that allow readers to directly understand the models discussed. It is published as part of the renowned Cognitive Technologies (COGTECH) series and is fully accessible via Springer Nature. The book covers everything from classic optimization problems to Hopfield networks and adiabatic quantum computation, using numerous examples to show how QUBOs and combinatorial optimization problems can be elegantly translated into the quantum world.