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Experimental realization of a quantum image classifier via tensor-network-based machine learning
- Source :
- Photonics Research 9 (12), 12002332 (2021)
- Publication Year :
- 2020
-
Abstract
- Quantum machine learning aspires to overcome intractability that currently limits its applicability to practical problems. However, quantum machine learning itself is limited by low effective dimensions achievable in state-of-the-art experiments. Here we demonstrate highly successful classifications of real-life images using photonic qubits, combining a quantum tensor-network representation of hand-written digits and entanglement-based optimization. Specifically, we focus on binary classification for hand-written zeroes and ones, whose features are cast into the tensor-network representation, further reduced by optimization based on entanglement entropy and encoded into two-qubit photonic states. We then demonstrate image classification with a high success rate exceeding 98%, through successive gate operations and projective measurements. Although we work with photons, our approach is amenable to other physical realizations such as nitrogen-vacancy centers, nuclear spins and trapped ions, and our scheme can be scaled to efficient multi-qubit encodings of features in the tensor-product representation, thereby setting the stage for quantum-enhanced multi-class classification.<br />Comment: 10 pages, 7 figures
- Subjects :
- Quantum Physics
Subjects
Details
- Database :
- arXiv
- Journal :
- Photonics Research 9 (12), 12002332 (2021)
- Publication Type :
- Report
- Accession number :
- edsarx.2003.08551
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1364/PRJ.434217