Variational Methods in Optical Quantum Machine Learning
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 o...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10325513/ |
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| Summary: | 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, <inline-formula> <tex-math notation="LaTeX">$x_{1}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$x_{2}$ </tex-math></inline-formula>, 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. |
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| ISSN: | 2169-3536 |