| Autor: |
M. S. Moreira, G. G. Guerreschi, W. Vlothuizen, J. F. Marques, J. van Straten, S. P. Premaratne, X. Zou, H. Ali, N. Muthusubramanian, C. Zachariadis, J. van Someren, M. Beekman, N. Haider, A. Bruno, C. G. Almudever, A. Y. Matsuura, L. DiCarlo |
| Jazyk: |
angličtina |
| Rok vydání: |
2023 |
| Předmět: |
|
| Zdroj: |
npj Quantum Information, Vol 9, Iss 1, Pp 1-7 (2023) |
| Druh dokumentu: |
article |
| ISSN: |
2056-6387 |
| DOI: |
10.1038/s41534-023-00779-5 |
| Popis: |
Abstract Artificial neural networks are becoming an integral part of digital solutions to complex problems. However, employing neural networks on quantum processors faces challenges related to the implementation of non-linear functions using quantum circuits. In this paper, we use repeat-until-success circuits enabled by real-time control-flow feedback to realize quantum neurons with non-linear activation functions. These neurons constitute elementary building blocks that can be arranged in a variety of layouts to carry out deep learning tasks quantum coherently. As an example, we construct a minimal feedforward quantum neural network capable of learning all 2-to-1-bit Boolean functions by optimization of network activation parameters within the supervised-learning paradigm. This model is shown to perform non-linear classification and effectively learns from multiple copies of a single training state consisting of the maximal superposition of all inputs. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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