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Robust Quantum Machine Learning Achieves Increased Accuracy on MNIST and FMNIST Datasets

Researchers at the University of Melbourne introduced a Matrix Product State (MPS) based encoding scheme that dramatically reduces quantum circuit depth while preserving classification performance. By iteratively applying singular value decomposition, the method creates low‑depth, approximate encodings that require fewer qubits than traditional one‑qubit‑per‑feature approaches. Experiments on the MNIST and FMNIST datasets showed improved accuracy and heightened resistance to classical adversarial attacks. A small‑scale test on a superconducting quantum processor confirmed the practicality of the technique for near‑term hardware.