Quantum Hopfield neural network
Name
PhysRevA.98.042308.pdf
Size
495.59 KB
Format
Adobe PDF
Checksum (MD5)
4512089d4d9b05d0dd6a07f831c1a42a
Author(s)
Rebentrost, Patrick
Bromley, Thomas R.
Weedbrook, Christian
Lloyd, Seth
Date Issued
October 2018
Journal
Physical review A
Publisher
American Physical Society
Citation
Rebentrost, Patrick, Thomas R. Bromley, Christian Weedbrook and Seth Lloyd. "Quantum Hopfield neural network." Phys. Rev. A 98, 042308 (2018)
Version
Final published version
Abstract
Quantum computing allows for the potential of significant advancements in both the speed and the capacity of widely used machine learning techniques. Here we employ quantum algorithms for the Hopfield network, which can be used for pattern recognition, reconstruction, and optimization as a realization of a content-addressable memory system. We show that an exponentially large network can be stored in a polynomial number of quantum bits by encoding the network into the amplitudes of quantum states. By introducing a classical technique for operating the Hopfield network, we can leverage quantum algorithms to obtain a quantum computational complexity that is logarithmic in the dimension of the data. We also present an application of our method as a genetic sequence recognizer.
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Department of Physics
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Massachusetts Institute of Technology. Research Laboratory of Electronics
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1103/PhysRevA.98.042308
