Special Issue "Quantum Machine Learning: Theory, Methods and Applications"
Deadline for manuscript submissions: closed (20 November 2022) | Viewed by 4019
Interests: Edge Computing; Quantum Computing
Interests: Quantum Machine Learning; Quantum Networks; Quantum Cloud
Interests: quantum computing; quantum machine learning; quantum optimal control; quantum error correction
Special Issues, Collections and Topics in MDPI journals
Quantum computing has promised a significant speedup in certain computationally intensive tasks that are intractable on classical computers. Famous examples are Shor’s algorithm, which can be used to factorize large numbers and can potentially threaten current state-of-the-art public-key cryptography systems, and Grover’s algorithm, which can provide a quadratic speedup in the unstructured database search.
Meanwhile, machine learning (ML) technologies are widely developed both in academia and industries. For example, ML technologies have been shown to be highly successful in computer vision, natural language processing and even mastering the game of Go with superhuman-level performance. Quantum machine learning (QML), a promising research direction, is to build an even more powerful AI/ML framework that can leverage the advantages provided by quantum physics. Notably, recent advances in quantum computing hardware shed light on the reality of such algorithms. Variational quantum circuits (VQC)-based quantum machine learning algorithms have been shown to be highly successful in various applications such as classification, function approximation, generative modeling, sequential modeling, federated learning and deep reinforcement learning. However, there are still several challenges to solve. For example, the optimization of VQC-based models can potentially suffer from the Barren plateaus, which hinder the scalability of QML models. On the other hand, works exist to map the existing neural networks to the quantum computers and demonstrate the potential quantum advantage that can be achieved for the quantum neuron via a co-design. However, it is still questionable whether such a design can be scaled up for deep learning. Therefore, it is highly non-trivial to design a proper QML architecture for specific commercial or scientific applications. Such difficulties make domain experts inaccessible to the power of quantum machine learning.
This Special Issue will include most aspects of QML research, such as theoretical investigation, practical software implementation and real-world applications. All paradigms of QML are welcome. This Special Issue will bring both quantum computing and classical machine learning together and shed light on promising new research directions as well as valuable applications for either industries or academia.
Dr. Weiwen Jiang
Dr. Ying Mao
Dr. Samuel Yen-Chi Chen
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
- Quantum machine learning
- Quantum neural network
- Quantum supervised learning
- Quantum unsupervised learning
- Quantum reinforcement learning
- Quantum learning theory
- Variational quantum circuits
- Noisy intermediate-scale quantum devices (NISQ)