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Machine Learning with Quantum Matter: An Example Using Lead Zirconate Titanate
- Source :
- Quantum Reports, Vol 4, Iss 4, Pp 418-433 (2022)
- Publication Year :
- 2022
- Publisher :
- MDPI AG, 2022.
-
Abstract
- Stephen Wolfram (2002) proposed the concept of computational equivalence, which implies that almost any dynamical system can be considered as a computation, including programmable matter and nonlinear materials such as, so called, quantum matter. Memristors are often used in building and evaluating hardware neural networks. Ukil (2011) demonstrated a theoretical relationship between piezoelectrical materials and memristors. We review that work as a necessary background prior to our work on exploring a piezoelectric material for neural network computation. Our method consisted of using a cubic block of unpoled lead zirconate titanate (PZT) ceramic, to which we have attached wires for programming the PZT as a programmable substrate. We then, by means of pulse trains, constructed on-the-fly internal patterns of regions of aligned polarization and unaligned, or disordered regions. These dynamic patterns come about through constructive and destructive interference and may be exploited as a type of reservoir network. Using MNIST data we demonstrate a learning machine.
Details
- Language :
- English
- ISSN :
- 2624960X
- Volume :
- 4
- Issue :
- 4
- Database :
- Directory of Open Access Journals
- Journal :
- Quantum Reports
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.b0e3a73ce5d5491fb3eff20c54a05939
- Document Type :
- article
- Full Text :
- https://doi.org/10.3390/quantum4040030