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Quantum machine learning aspires to overcome intractability that currentlylimits its applicability to practical problems. However, quantum machinelearning itself is limited by low effective dimensions achievable instate-of-the-art experiments. Here we demonstrate highly successfulclassifications of real-life images using photonic qubits, combining a quantumtensor-network representation of hand-written digits and entanglement-basedoptimization. Specifically, we focus on binary classification for hand-writtenzeroes and ones, whose features are cast into the tensor-networkrepresentation, further reduced by optimization based on entanglement entropyand encoded into two-qubit photonic states. We then demonstrate imageclassification with a high success rate exceeding 98%, through successive gateoperations and projective measurements. Although we work with photons, ourapproach is amenable to other physical realizations such as nitrogen-vacancycenters, nuclear spins and trapped ions, and our scheme can be scaled toefficient multi-qubit encodings of features in the tensor-productrepresentation, thereby setting the stage for quantum-enhanced multi-classclassification.

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