The Future of Quantum Machine Learning and Quantum AI
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Quantum Information".
Deadline for manuscript submissions: 20 December 2024 | Viewed by 584
Special Issue Editor
Interests: machine learning; artificial intelligence; quantum computing
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The use of quantum coprocessors for extensive and non-tractable computation routines in AI will lead to new machine learning and artificial intelligence applications.
However, we need a deeper understanding of the mathematical framework and the resulting constraints. What are the quantum machine learning applications? What are the advantages of quantum machine learning algorithms to combat various proposed artificial problems? Can we apply quantum machine learning and quantum AI for real-world applications?
Linear algebra-based quantum machine learning is based on quantum gates that describe quantum basic linear algebra subroutines. These subroutines exhibit theoretical exponential speedups compared to their classical counterparts and are essential for machine learning. The quantum algorithm for linear systems of equations is one of the main fundamental algorithms expected to provide an increase in speed compared to traditional algorithms. The algorithm is also called the HHL algorithm and is based on Kitaev’s phase algorithm. Quantum principal component analysis (qPCA) and quantum random-access memory (qRAM) have been previously described, and quantum kernels and quantum advantage kernels have already been introduced and identified. Still, there are many open problems, such as the efficient preparation of data or the estimation of the expected values that describe the results.
We discussed these problems in the Special Issue "Quantum Machine Learning 2022" and made much progress. Based on its success, we are continuing this trend with the current Special Issue "The Future of Quantum Machine Learning and Quantum AI". To view the Special Issue "Quantum Machine Learning 2022", please consult the following link:
https://www.mdpi.com/journal/entropy/special_issues/quantum_machine_learning_2022
Prof. Dr. Andreas (Andrzej) Wichert
Guest Editor
Manuscript Submission Information
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Keywords
- quantum-inspired machine learning
- quantum-inspired AI
- quantum genetic algorithms
- quantum machine learning applications
- linear algebra-based quantum machine learning
- quantum kernels
- efficient preparation of data
- quantum programming languages
- variational algorithms
- quantum decision trees
- quantum neural networks
- quantum annealing
