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

Understand

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.