Quantum learning with noise and decoherence: A robust quantum neural network
Behrman, Elizabeth C. Nguyen, Nam H. Steck, James E.
Behrman, Elizabeth C.
Nguyen, Nam H.
Steck, James E.
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Issue Date
2020-01-31
Type
Article
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Keywords
Quantum computing,Entanglement,Dynamic learning,Noise,Decoherence,Bootstrap,Pattern storage
Subjects (LCSH)
Citation
Nguyen, N.H., Behrman, E.C. & Steck, J.E. Quantum learning with noise and decoherence: a robust quantum neural network. Quantum Mach. Intell. 2, 1 (2020). https://doi.org/10.1007/s42484-020-00013-x
Abstract
Noise and decoherence are two major obstacles to the implementation of large-scale quantum computing. Because of the no-cloning theorem, which says we cannot make an exact copy of an arbitrary quantum state, simple redundancy will not work in a quantum context, and unwanted interactions with the environment can destroy coherence and thus the quantum nature of the computation. Because of the parallel and distributed nature of classical neural networks, they have long been successfully used to deal with incomplete or damaged data. In this work, we show that our model of a quantum neural network (QNN) is similarly robust to noise, and that, in addition, it is robust to decoherence. Moreover, robustness to noise and decoherence is not only maintained but improved as the size of the system is increased. Noise and decoherence may even be of advantage in training, as it helps correct for overfitting. We demonstrate the robustness using entanglement as a means for pattern storage in a qubit array. Our results provide evidence that machine learning approaches can obviate otherwise recalcitrant problems in quantum computing.
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The article can be found here: https://rdcu.be/ezUA7
Publisher
Springer Nature
Journal
Quantum Machine Intelligence
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Digital Collection
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Archival Collection
PubMed ID
ISSN
2524-4914