Benchmarking Neural Networks For Quantum Computations.

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ID: 37473
2019
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Ranked #42 of 56 articles by views in IEEE Transactions on Neural Networks and Learning Systems

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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.
Reference Key
nguyen2019benchmarkingieee Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Nguyen, Nam H;Behrman, E C;Moustafa, Mohamed A;Steck, J E;
Journal IEEE Transactions on Neural Networks and Learning Systems
Year 2019
DOI
10.1109/TNNLS.2019.2933394
URL
Keywords Keywords not found

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