Benchmarking neural networks for quantum computations
Nguyen, Nam H. Behrman, Elizabeth C. Moustafa, Mohamed A. Steck, James E.
Nguyen, Nam H.
Behrman, Elizabeth C.
Moustafa, Mohamed A.
Steck, James E.
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Time Period
Advisors
Original Date
Digitization Date
Issue Date
2019-09-02
Type
Article
Genre
Keywords
Benchmarking,Complex neural network,Complexity,Entanglement,Quantum computation,Quantum machine learning,Quantum neural network,Biological neural networks
Subjects (LCSH)
Citation
N. H. Nguyen, E. C. Behrman, M. A. Moustafa and J. E. Steck, "Benchmarking Neural Networks For Quantum Computations," in IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 7, pp. 2522-2531, July 2020, doi: 10.1109/TNNLS.2019.2933394
Abstract
The power of quantum computers is still somewhat speculative. Although they are certainly faster than classical ones at some tasks, the class of problems they can efficiently solve has not been mapped definitively onto known classical complexity theory. This means that we do not know for which calculations there will be a “quantum advantage,” once an algorithm is found. One way to answer the question is to find those algorithms, but finding truly quantum algorithms turns out to be very difficult. In previous work, over the past three decades, we have pursued the idea of using techniques of machine learning to develop algorithms for quantum computing. Here, we compare the performance of standard real- and complex-valued classical neural networks with that of one of our models for a quantum neural network, on both classical problems and on an archetypal quantum problem: the computation of an entanglement witness. The quantum network is shown to need far fewer epochs and a much smaller network to achieve comparable or better results.
Table of Contents
Description
The article can be found here: https://ieeexplore.ieee.org/document/8822629
Publisher
IEEE
Journal
IEEE Transactions on Neural Networks and Learning Systems
Book Title
Series
Digital Collection
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Archival Collection
PubMed ID
ISSN
2162-237X
2162-2388
2162-2388