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This paper presents a fair-comparison study of variational quantum circuits in diffusion models, introducing a squeeze-and-excitation scaffold to isolate quantum contributions. It finds functional parity with classical controls and identifies angle-embedding failures in score-based settings, offering a rigorous methodology and mechanistic analysis.
This paper introduces Quiver, a paradigm that enriches classical machine learning models with quantum-inspired features derived from the quantum Fisher information matrix, demonstrating improvements on molecule property prediction and jet flavor classification benchmarks.
This paper presents empirical evidence that quantum entanglement provides a measurable advantage in multi-agent reinforcement learning, using the CHSH game and cooperative navigation tasks to demonstrate performance improvements over classical baselines.