A variational autoencoder learns quantum embeddings compressing ImageNet into 13 qubits and achieving 98.5% accuracy on MNIST 3-vs-5 classification with a quantum circuit, close to classical baselines and far above naive amplitude embeddings.
Svore, and Nathan Wiebe
5 Pith papers cite this work. Polarity classification is still indexing.
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Hybrid quantum-classical FBPINN for acoustic FWI achieves lower L1 velocity error than classical baselines in ~8x fewer iterations with ~33% fewer parameters on anomaly and checkerboard benchmarks.
QML-PipeGuard is a framework for runtime behavioral fingerprinting of QML pipelines that absorbs benign drift while detecting adversarial channel substitution via informationally complete measurements.
A new QNN architecture with unified graph, HAL, and ONNX pipeline enables cross-framework and cross-hardware QML with training time within 8% of native implementations and identical accuracy on Iris, Wine, and MNIST-4 tasks.
The authors present Pilot-Quantum, a middleware for adaptive resource management in hybrid quantum-HPC systems, along with execution motifs and a performance modeling toolkit called Q-Dreamer.
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Eliminating Vendor Lock-In in Quantum Machine Learning via Framework-Agnostic Neural Networks
A new QNN architecture with unified graph, HAL, and ONNX pipeline enables cross-framework and cross-hardware QML with training time within 8% of native implementations and identical accuracy on Iris, Wine, and MNIST-4 tasks.