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Variational Methods in Optical Quantum Machine Learning
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DOI：10.1109/ACCESS.2023.3335625.png)
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The computing world is rapidly evolving and advancing, with new ground-breaking technologies emerging. Quantum Computing and Quantum Machine Learning have opened up new possibilities, providing unprecedented computational power and problem-solving capabilities while offering a deeper understanding of complex systems. Our research proposes new variational methods based on a deep learning system based on an optical quantum neural network applied to Machine Learning models for point classification. As a case study, we considered the binary classification of points belonging to a certain geometric pattern (the Two-Moons Classification problem) on a plane. We think it is reasonable to expect benefits from using hybrid deep learning systems (classical + quantum), not just in terms of accelerating computation but also in understanding the underlying phenomena and mechanisms. This will result in the development of new machine-learning paradigms and a significant advancement in the field of quantum computation. The selected dataset is a set of 2D points creating two interleaved semicircles and is based on a 2D binary classification generator, which aids in evaluating the performance of particular methods. The two coordinates of each unique point, x(1) and x(2), serve as the features since they present two disparate data sets in a two-dimensional representation space. The goal was to create a quantum deep neural network that could recognise and categorise points accurately with the fewest trainable parameters possible.
Keyword:
Quantum computing
variational methods
deep learning
quantum feed-forward neural networks
optical quantum computing
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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引用论文
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