A value-iteration network with global phase invariance, unitary perturbation augmentation, and NeRF-style encoding synthesizes 2- and 3-qubit Clifford+T circuits with fewer T gates than the Quantum Shannon Decomposition baseline.
Multi-Spectral Image Classification with Ultra-Lean Complex-Valued Models
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abstract
Multi-spectral imagery is invaluable for remote sensing due to different spectral signatures exhibited by materials that often appear identical in greyscale and RGB imagery. Paired with modern deep learning methods, this modality has great potential utility in a variety of remote sensing applications, such as humanitarian assistance and disaster recovery efforts. State-of-the-art deep learning methods have greatly benefited from large-scale annotations like in ImageNet, but existing MSI image datasets lack annotations at a similar scale. As an alternative to transfer learning on such data with few annotations, we apply complex-valued co-domain symmetric models to classify real-valued MSI images. Our experiments on 8-band xView data show that our ultra-lean model trained on xView from scratch without data augmentations can outperform ResNet with data augmentation and modified transfer learning on xView. Our work is the first to demonstrate the value of complex-valued deep learning on real-valued MSI data.
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Making Neural Networks More Suitable for Approximate Clifford+T Circuit Synthesis
A value-iteration network with global phase invariance, unitary perturbation augmentation, and NeRF-style encoding synthesizes 2- and 3-qubit Clifford+T circuits with fewer T gates than the Quantum Shannon Decomposition baseline.