Matrix Product States Provide Robust Quantum Encodings
A Jan. 14, 2026 arXiv preprint by Muhammad Usman introduces using Matrix Product State (MPS) representations to construct low-depth quantum circuits that encode classical data for quantum machine learning. The paper shows the approximate low-depth encoding preserves classification accuracy while increasing robustness to classical adversarial attacks. The authors demonstrate adversarially robust variational quantum classifiers on MNIST and FMNIST and report a small superconducting-device experiment.
Key Points
- 1Introduce MPS-based circuit construction for encoding classical data into low-depth quantum states
- 2Demonstrate low-depth encoding preserves classification accuracy and increases robustness to adversarial attacks
- 3Enable practitioners to deploy shallower variational quantum classifiers on MNIST, FMNIST, and real hardware
Scoring Rationale
Novel, experimentally validated MPS encoding drives the score; limited by single arXiv preprint and pending peer review.
Sources
Public references used for this report.
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