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A unified embedding framework is introduced that formulates embedding as a functional transformation shaping the feature representation available to the variational circuit, and two embeddings within it are proposed.
This work proposes a multimodal anomaly detection framework based on cross-modal reconstruction of heterogeneous time-series sensor data that learns system dynamics by reconstructing each modality from the others, thereby exploiting complementary information across modalities.
A variational quantum framework for finite-horizon quantum control based on hardware-efficient ans\"atze, providing a flexible and implementation-friendly approach compatible with near-term quantum devices.
A mathematical representation of the output data is developed using Koopman operator theory, which motivates their embedding on a manifold and its subsequent approximation with a two-stage autoencoder, which provides favorable fault-detection performance compared with standard autoencoders.
A quantum-inspired tensor-network framework for solving advection–diffusion–reaction (ADR) partial differential equations and results highlight the potential of tensor networks as efficient structure-preserving tools for PDE simulation in multiple spatial dimensions.
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