Experimental realization of a quantum image classifier via tensor-network-based machine learning

• URL: http://arxiv.org/abs/2003.08551v2
• Date: Sun, 2 Jan 2022 09:23:34 GMT
• Title: Experimental realization of a quantum image classifier via tensor-network-based machine learning
• Authors: Kunkun Wang, Lei Xiao, Wei Yi, Shi-Ju Ran, Peng Xue
• Abstract summary: We demonstrate highly successful classifications of real-life images using photonic qubits. We focus on binary classification for hand-written zeroes and ones, whose features are cast into the tensor-network representation. Our scheme can be scaled to efficient multi-qubit encodings of features in the tensor-product representation.
• Score: 4.030017427802459
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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