The curse of dimensionality has historically limited the ability to represent and compute high-dimensional, multi-modal, non-convex distributions. This work introduces a hybrid-classical / quantum-inspired latent solver that overcomes these limitations by combining: Classical neural encoders for latent amplitudes Quantum-inspired superposition of latent modes Tensor-network or variational quantum circuit decoders to capture cross-mode correlations Dynamic latent mode pruning and resource optimization We demonstrate practical curse-breaking with full simulations tracking hundreds of latent modes, showing scalable, parallel evaluation of high-dimensional systems. The framework is ready for extension to real quantum hardware and provides a blueprint for infinite-dimensional latent representation. All code, simulations, and derivations are included, making the research fully reproducible. Keywords curse of dimensionality, high-dimensional systems, hybrid-classical quantum computing, variational quantum circuits, latent variable models, tensor networks, multi-modal distributions