Entangled q-Convolutional Neural Nets
| Autor: | Vassilis Anagiannis, Miranda C. N. Cheng |
|---|---|
| Přispěvatelé: | String Theory (ITFA, IoP, FNWI), ITFA (IoP, FNWI) |
| Rok vydání: | 2021 |
| Předmět: |
Computer Science::Machine Learning
FOS: Computer and information sciences Computer Science - Machine Learning Theoretical computer science Computer science Computer Science::Neural and Evolutionary Computation FOS: Physical sciences Machine Learning (stat.ML) Quantum entanglement 01 natural sciences Convolutional neural network 010305 fluids & plasmas Machine Learning (cs.LG) Statistics - Machine Learning Artificial Intelligence Quantum state 0103 physical sciences Entropy (information theory) 010306 general physics Quantum Physics Artificial neural network TheoryofComputation_GENERAL Function (mathematics) Human-Computer Interaction Key (cryptography) Quantum Physics (quant-ph) Software MNIST database |
| Zdroj: | Machine Learning: Science and Technology, 2(4):045026. IOP |
| ISSN: | 2632-2153 |
| DOI: | 10.48550/arxiv.2103.11785 |
| Popis: | We introduce a machine learning model, the q-CNN model, sharing key features with convolutional neural networks and admitting a tensor network description. As examples, we apply q-CNN to the MNIST and Fashion MNIST classification tasks. We explain how the network associates a quantum state to each classification label, and study the entanglement structure of these network states. In both our experiments on the MNIST and Fashion-MNIST datasets, we observe a distinct increase in both the left/right as well as the up/down bipartition entanglement entropy (EE) during training as the network learns the fine features of the data. More generally, we observe a universal negative correlation between the value of the EE and the value of the cost function, suggesting that the network needs to learn the entanglement structure in order the perform the task accurately. This supports the possibility of exploiting the entanglement structure as a guide to design the machine learning algorithm suitable for given tasks. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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